diff options
Diffstat (limited to '.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane')
33 files changed, 16184 insertions, 0 deletions
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/__init__.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/__init__.py new file mode 100644 index 00000000..da466144 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/__init__.py @@ -0,0 +1,18 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) AutoRest Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +from ._azure_machine_learning_workspaces import AzureMachineLearningWorkspaces +from ._version import VERSION + +__version__ = VERSION +__all__ = ['AzureMachineLearningWorkspaces'] + +# `._patch.py` is used for handwritten extensions to the generated code +# Example: https://github.com/Azure/azure-sdk-for-python/blob/main/doc/dev/customize_code/how-to-patch-sdk-code.md +from ._patch import patch_sdk +patch_sdk() diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/_azure_machine_learning_workspaces.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/_azure_machine_learning_workspaces.py new file mode 100644 index 00000000..faa760c5 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/_azure_machine_learning_workspaces.py @@ -0,0 +1,117 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) AutoRest Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +from copy import deepcopy +from typing import TYPE_CHECKING + +from azure.mgmt.core import ARMPipelineClient +from msrest import Deserializer, Serializer + +from . import models +from ._configuration import AzureMachineLearningWorkspacesConfiguration +from .operations import DataCallOperations, DataContainerOperations, DataVersionOperations, DatasetControllerV2Operations, DatasetV2Operations, DatasetsV1Operations, DeleteOperations, GetOperationStatusOperations + +if TYPE_CHECKING: + # pylint: disable=unused-import,ungrouped-imports + from typing import Any, Optional + + from azure.core.credentials import TokenCredential + from azure.core.rest import HttpRequest, HttpResponse + +class AzureMachineLearningWorkspaces(object): + """AzureMachineLearningWorkspaces. + + :ivar data_call: DataCallOperations operations + :vartype data_call: azure.mgmt.machinelearningservices.operations.DataCallOperations + :ivar data_container: DataContainerOperations operations + :vartype data_container: azure.mgmt.machinelearningservices.operations.DataContainerOperations + :ivar delete: DeleteOperations operations + :vartype delete: azure.mgmt.machinelearningservices.operations.DeleteOperations + :ivar datasets_v1: DatasetsV1Operations operations + :vartype datasets_v1: azure.mgmt.machinelearningservices.operations.DatasetsV1Operations + :ivar dataset_controller_v2: DatasetControllerV2Operations operations + :vartype dataset_controller_v2: + azure.mgmt.machinelearningservices.operations.DatasetControllerV2Operations + :ivar dataset_v2: DatasetV2Operations operations + :vartype dataset_v2: azure.mgmt.machinelearningservices.operations.DatasetV2Operations + :ivar data_version: DataVersionOperations operations + :vartype data_version: azure.mgmt.machinelearningservices.operations.DataVersionOperations + :ivar get_operation_status: GetOperationStatusOperations operations + :vartype get_operation_status: + azure.mgmt.machinelearningservices.operations.GetOperationStatusOperations + :param credential: Credential needed for the client to connect to Azure. + :type credential: ~azure.core.credentials.TokenCredential + :param base_url: Service URL. Default value is ''. + :type base_url: str + :keyword int polling_interval: Default waiting time between two polls for LRO operations if no + Retry-After header is present. + """ + + def __init__( + self, + credential, # type: "TokenCredential" + base_url="", # type: str + **kwargs # type: Any + ): + # type: (...) -> None + self._config = AzureMachineLearningWorkspacesConfiguration(credential=credential, **kwargs) + self._client = ARMPipelineClient(base_url=base_url, config=self._config, **kwargs) + + client_models = {k: v for k, v in models.__dict__.items() if isinstance(v, type)} + self._serialize = Serializer(client_models) + self._deserialize = Deserializer(client_models) + self._serialize.client_side_validation = False + self.data_call = DataCallOperations(self._client, self._config, self._serialize, self._deserialize) + self.data_container = DataContainerOperations(self._client, self._config, self._serialize, self._deserialize) + self.delete = DeleteOperations(self._client, self._config, self._serialize, self._deserialize) + self.datasets_v1 = DatasetsV1Operations(self._client, self._config, self._serialize, self._deserialize) + self.dataset_controller_v2 = DatasetControllerV2Operations(self._client, self._config, self._serialize, self._deserialize) + self.dataset_v2 = DatasetV2Operations(self._client, self._config, self._serialize, self._deserialize) + self.data_version = DataVersionOperations(self._client, self._config, self._serialize, self._deserialize) + self.get_operation_status = GetOperationStatusOperations(self._client, self._config, self._serialize, self._deserialize) + + + def _send_request( + self, + request, # type: HttpRequest + **kwargs # type: Any + ): + # type: (...) -> HttpResponse + """Runs the network request through the client's chained policies. + + >>> from azure.core.rest import HttpRequest + >>> request = HttpRequest("GET", "https://www.example.org/") + <HttpRequest [GET], url: 'https://www.example.org/'> + >>> response = client._send_request(request) + <HttpResponse: 200 OK> + + For more information on this code flow, see https://aka.ms/azsdk/python/protocol/quickstart + + :param request: The network request you want to make. Required. + :type request: ~azure.core.rest.HttpRequest + :keyword bool stream: Whether the response payload will be streamed. Defaults to False. + :return: The response of your network call. Does not do error handling on your response. + :rtype: ~azure.core.rest.HttpResponse + """ + + request_copy = deepcopy(request) + request_copy.url = self._client.format_url(request_copy.url) + return self._client.send_request(request_copy, **kwargs) + + def close(self): + # type: () -> None + self._client.close() + + def __enter__(self): + # type: () -> AzureMachineLearningWorkspaces + self._client.__enter__() + return self + + def __exit__(self, *exc_details): + # type: (Any) -> None + self._client.__exit__(*exc_details) diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/_configuration.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/_configuration.py new file mode 100644 index 00000000..2ec7eb9e --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/_configuration.py @@ -0,0 +1,64 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) AutoRest Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +from typing import TYPE_CHECKING + +from azure.core.configuration import Configuration +from azure.core.pipeline import policies +from azure.mgmt.core.policies import ARMChallengeAuthenticationPolicy, ARMHttpLoggingPolicy + +from ._version import VERSION + +if TYPE_CHECKING: + # pylint: disable=unused-import,ungrouped-imports + from typing import Any + + from azure.core.credentials import TokenCredential + + +class AzureMachineLearningWorkspacesConfiguration(Configuration): + """Configuration for AzureMachineLearningWorkspaces. + + Note that all parameters used to create this instance are saved as instance + attributes. + + :param credential: Credential needed for the client to connect to Azure. + :type credential: ~azure.core.credentials.TokenCredential + """ + + def __init__( + self, + credential, # type: "TokenCredential" + **kwargs # type: Any + ): + # type: (...) -> None + super(AzureMachineLearningWorkspacesConfiguration, self).__init__(**kwargs) + if credential is None: + raise ValueError("Parameter 'credential' must not be None.") + + self.credential = credential + self.credential_scopes = kwargs.pop('credential_scopes', ['https://management.azure.com/.default']) + kwargs.setdefault('sdk_moniker', 'mgmt-machinelearningservices/{}'.format(VERSION)) + self._configure(**kwargs) + + def _configure( + self, + **kwargs # type: Any + ): + # type: (...) -> None + self.user_agent_policy = kwargs.get('user_agent_policy') or policies.UserAgentPolicy(**kwargs) + self.headers_policy = kwargs.get('headers_policy') or policies.HeadersPolicy(**kwargs) + self.proxy_policy = kwargs.get('proxy_policy') or policies.ProxyPolicy(**kwargs) + self.logging_policy = kwargs.get('logging_policy') or policies.NetworkTraceLoggingPolicy(**kwargs) + self.http_logging_policy = kwargs.get('http_logging_policy') or ARMHttpLoggingPolicy(**kwargs) + self.retry_policy = kwargs.get('retry_policy') or policies.RetryPolicy(**kwargs) + self.custom_hook_policy = kwargs.get('custom_hook_policy') or policies.CustomHookPolicy(**kwargs) + self.redirect_policy = kwargs.get('redirect_policy') or policies.RedirectPolicy(**kwargs) + self.authentication_policy = kwargs.get('authentication_policy') + if self.credential and not self.authentication_policy: + self.authentication_policy = ARMChallengeAuthenticationPolicy(self.credential, *self.credential_scopes, **kwargs) diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/_patch.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/_patch.py new file mode 100644 index 00000000..74e48ecd --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/_patch.py @@ -0,0 +1,31 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# +# Copyright (c) Microsoft Corporation. All rights reserved. +# +# The MIT License (MIT) +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the ""Software""), to +# deal in the Software without restriction, including without limitation the +# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or +# sell copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in +# all copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING +# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS +# IN THE SOFTWARE. +# +# -------------------------------------------------------------------------- + +# This file is used for handwritten extensions to the generated code. Example: +# https://github.com/Azure/azure-sdk-for-python/blob/main/doc/dev/customize_code/how-to-patch-sdk-code.md +def patch_sdk(): + pass \ No newline at end of file diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/_vendor.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/_vendor.py new file mode 100644 index 00000000..138f663c --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/_vendor.py @@ -0,0 +1,27 @@ +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) AutoRest Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +from azure.core.pipeline.transport import HttpRequest + +def _convert_request(request, files=None): + data = request.content if not files else None + request = HttpRequest(method=request.method, url=request.url, headers=request.headers, data=data) + if files: + request.set_formdata_body(files) + return request + +def _format_url_section(template, **kwargs): + components = template.split("/") + while components: + try: + return template.format(**kwargs) + except KeyError as key: + formatted_components = template.split("/") + components = [ + c for c in formatted_components if "{}".format(key.args[0]) not in c + ] + template = "/".join(components) diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/_version.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/_version.py new file mode 100644 index 00000000..eae7c95b --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/_version.py @@ -0,0 +1,9 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) AutoRest Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +VERSION = "0.1.0" diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/__init__.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/__init__.py new file mode 100644 index 00000000..f67ccda9 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/__init__.py @@ -0,0 +1,15 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) AutoRest Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +from ._azure_machine_learning_workspaces import AzureMachineLearningWorkspaces +__all__ = ['AzureMachineLearningWorkspaces'] + +# `._patch.py` is used for handwritten extensions to the generated code +# Example: https://github.com/Azure/azure-sdk-for-python/blob/main/doc/dev/customize_code/how-to-patch-sdk-code.md +from ._patch import patch_sdk +patch_sdk() diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/_azure_machine_learning_workspaces.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/_azure_machine_learning_workspaces.py new file mode 100644 index 00000000..597cca3d --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/_azure_machine_learning_workspaces.py @@ -0,0 +1,111 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) AutoRest Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +from copy import deepcopy +from typing import Any, Awaitable, Optional, TYPE_CHECKING + +from azure.core.rest import AsyncHttpResponse, HttpRequest +from azure.mgmt.core import AsyncARMPipelineClient +from msrest import Deserializer, Serializer + +from .. import models +from ._configuration import AzureMachineLearningWorkspacesConfiguration +from .operations import DataCallOperations, DataContainerOperations, DataVersionOperations, DatasetControllerV2Operations, DatasetV2Operations, DatasetsV1Operations, DeleteOperations, GetOperationStatusOperations + +if TYPE_CHECKING: + # pylint: disable=unused-import,ungrouped-imports + from azure.core.credentials_async import AsyncTokenCredential + +class AzureMachineLearningWorkspaces: + """AzureMachineLearningWorkspaces. + + :ivar data_call: DataCallOperations operations + :vartype data_call: azure.mgmt.machinelearningservices.aio.operations.DataCallOperations + :ivar data_container: DataContainerOperations operations + :vartype data_container: + azure.mgmt.machinelearningservices.aio.operations.DataContainerOperations + :ivar delete: DeleteOperations operations + :vartype delete: azure.mgmt.machinelearningservices.aio.operations.DeleteOperations + :ivar datasets_v1: DatasetsV1Operations operations + :vartype datasets_v1: azure.mgmt.machinelearningservices.aio.operations.DatasetsV1Operations + :ivar dataset_controller_v2: DatasetControllerV2Operations operations + :vartype dataset_controller_v2: + azure.mgmt.machinelearningservices.aio.operations.DatasetControllerV2Operations + :ivar dataset_v2: DatasetV2Operations operations + :vartype dataset_v2: azure.mgmt.machinelearningservices.aio.operations.DatasetV2Operations + :ivar data_version: DataVersionOperations operations + :vartype data_version: azure.mgmt.machinelearningservices.aio.operations.DataVersionOperations + :ivar get_operation_status: GetOperationStatusOperations operations + :vartype get_operation_status: + azure.mgmt.machinelearningservices.aio.operations.GetOperationStatusOperations + :param credential: Credential needed for the client to connect to Azure. + :type credential: ~azure.core.credentials_async.AsyncTokenCredential + :param base_url: Service URL. Default value is ''. + :type base_url: str + :keyword int polling_interval: Default waiting time between two polls for LRO operations if no + Retry-After header is present. + """ + + def __init__( + self, + credential: "AsyncTokenCredential", + base_url: str = "", + **kwargs: Any + ) -> None: + self._config = AzureMachineLearningWorkspacesConfiguration(credential=credential, **kwargs) + self._client = AsyncARMPipelineClient(base_url=base_url, config=self._config, **kwargs) + + client_models = {k: v for k, v in models.__dict__.items() if isinstance(v, type)} + self._serialize = Serializer(client_models) + self._deserialize = Deserializer(client_models) + self._serialize.client_side_validation = False + self.data_call = DataCallOperations(self._client, self._config, self._serialize, self._deserialize) + self.data_container = DataContainerOperations(self._client, self._config, self._serialize, self._deserialize) + self.delete = DeleteOperations(self._client, self._config, self._serialize, self._deserialize) + self.datasets_v1 = DatasetsV1Operations(self._client, self._config, self._serialize, self._deserialize) + self.dataset_controller_v2 = DatasetControllerV2Operations(self._client, self._config, self._serialize, self._deserialize) + self.dataset_v2 = DatasetV2Operations(self._client, self._config, self._serialize, self._deserialize) + self.data_version = DataVersionOperations(self._client, self._config, self._serialize, self._deserialize) + self.get_operation_status = GetOperationStatusOperations(self._client, self._config, self._serialize, self._deserialize) + + + def _send_request( + self, + request: HttpRequest, + **kwargs: Any + ) -> Awaitable[AsyncHttpResponse]: + """Runs the network request through the client's chained policies. + + >>> from azure.core.rest import HttpRequest + >>> request = HttpRequest("GET", "https://www.example.org/") + <HttpRequest [GET], url: 'https://www.example.org/'> + >>> response = await client._send_request(request) + <AsyncHttpResponse: 200 OK> + + For more information on this code flow, see https://aka.ms/azsdk/python/protocol/quickstart + + :param request: The network request you want to make. Required. + :type request: ~azure.core.rest.HttpRequest + :keyword bool stream: Whether the response payload will be streamed. Defaults to False. + :return: The response of your network call. Does not do error handling on your response. + :rtype: ~azure.core.rest.AsyncHttpResponse + """ + + request_copy = deepcopy(request) + request_copy.url = self._client.format_url(request_copy.url) + return self._client.send_request(request_copy, **kwargs) + + async def close(self) -> None: + await self._client.close() + + async def __aenter__(self) -> "AzureMachineLearningWorkspaces": + await self._client.__aenter__() + return self + + async def __aexit__(self, *exc_details) -> None: + await self._client.__aexit__(*exc_details) diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/_configuration.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/_configuration.py new file mode 100644 index 00000000..26def54e --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/_configuration.py @@ -0,0 +1,60 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) AutoRest Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +from typing import Any, TYPE_CHECKING + +from azure.core.configuration import Configuration +from azure.core.pipeline import policies +from azure.mgmt.core.policies import ARMHttpLoggingPolicy, AsyncARMChallengeAuthenticationPolicy + +from .._version import VERSION + +if TYPE_CHECKING: + # pylint: disable=unused-import,ungrouped-imports + from azure.core.credentials_async import AsyncTokenCredential + + +class AzureMachineLearningWorkspacesConfiguration(Configuration): + """Configuration for AzureMachineLearningWorkspaces. + + Note that all parameters used to create this instance are saved as instance + attributes. + + :param credential: Credential needed for the client to connect to Azure. + :type credential: ~azure.core.credentials_async.AsyncTokenCredential + """ + + def __init__( + self, + credential: "AsyncTokenCredential", + **kwargs: Any + ) -> None: + super(AzureMachineLearningWorkspacesConfiguration, self).__init__(**kwargs) + if credential is None: + raise ValueError("Parameter 'credential' must not be None.") + + self.credential = credential + self.credential_scopes = kwargs.pop('credential_scopes', ['https://management.azure.com/.default']) + kwargs.setdefault('sdk_moniker', 'mgmt-machinelearningservices/{}'.format(VERSION)) + self._configure(**kwargs) + + def _configure( + self, + **kwargs: Any + ) -> None: + self.user_agent_policy = kwargs.get('user_agent_policy') or policies.UserAgentPolicy(**kwargs) + self.headers_policy = kwargs.get('headers_policy') or policies.HeadersPolicy(**kwargs) + self.proxy_policy = kwargs.get('proxy_policy') or policies.ProxyPolicy(**kwargs) + self.logging_policy = kwargs.get('logging_policy') or policies.NetworkTraceLoggingPolicy(**kwargs) + self.http_logging_policy = kwargs.get('http_logging_policy') or ARMHttpLoggingPolicy(**kwargs) + self.retry_policy = kwargs.get('retry_policy') or policies.AsyncRetryPolicy(**kwargs) + self.custom_hook_policy = kwargs.get('custom_hook_policy') or policies.CustomHookPolicy(**kwargs) + self.redirect_policy = kwargs.get('redirect_policy') or policies.AsyncRedirectPolicy(**kwargs) + self.authentication_policy = kwargs.get('authentication_policy') + if self.credential and not self.authentication_policy: + self.authentication_policy = AsyncARMChallengeAuthenticationPolicy(self.credential, *self.credential_scopes, **kwargs) diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/_patch.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/_patch.py new file mode 100644 index 00000000..74e48ecd --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/_patch.py @@ -0,0 +1,31 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# +# Copyright (c) Microsoft Corporation. All rights reserved. +# +# The MIT License (MIT) +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the ""Software""), to +# deal in the Software without restriction, including without limitation the +# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or +# sell copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in +# all copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING +# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS +# IN THE SOFTWARE. +# +# -------------------------------------------------------------------------- + +# This file is used for handwritten extensions to the generated code. Example: +# https://github.com/Azure/azure-sdk-for-python/blob/main/doc/dev/customize_code/how-to-patch-sdk-code.md +def patch_sdk(): + pass \ No newline at end of file diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/__init__.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/__init__.py new file mode 100644 index 00000000..f0340813 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/__init__.py @@ -0,0 +1,27 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) AutoRest Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +from ._data_call_operations import DataCallOperations +from ._data_container_operations import DataContainerOperations +from ._delete_operations import DeleteOperations +from ._datasets_v1_operations import DatasetsV1Operations +from ._dataset_controller_v2_operations import DatasetControllerV2Operations +from ._dataset_v2_operations import DatasetV2Operations +from ._data_version_operations import DataVersionOperations +from ._get_operation_status_operations import GetOperationStatusOperations + +__all__ = [ + 'DataCallOperations', + 'DataContainerOperations', + 'DeleteOperations', + 'DatasetsV1Operations', + 'DatasetControllerV2Operations', + 'DatasetV2Operations', + 'DataVersionOperations', + 'GetOperationStatusOperations', +] diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_data_call_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_data_call_operations.py new file mode 100644 index 00000000..cf00280c --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_data_call_operations.py @@ -0,0 +1,243 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) AutoRest Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +import functools +from typing import Any, Callable, Dict, Generic, List, Optional, TypeVar +import warnings + +from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error +from azure.core.pipeline import PipelineResponse +from azure.core.pipeline.transport import AsyncHttpResponse +from azure.core.rest import HttpRequest +from azure.core.tracing.decorator_async import distributed_trace_async +from azure.mgmt.core.exceptions import ARMErrorFormat + +from ... import models as _models +from ..._vendor import _convert_request +from ...operations._data_call_operations import build_get_preview_for_ml_table_request, build_get_quick_profile_for_ml_table_request, build_get_schema_for_ml_table_request +T = TypeVar('T') +ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]] + +class DataCallOperations: + """DataCallOperations async operations. + + You should not instantiate this class directly. Instead, you should create a Client instance that + instantiates it for you and attaches it as an attribute. + + :ivar models: Alias to model classes used in this operation group. + :type models: ~azure.mgmt.machinelearningservices.models + :param client: Client for service requests. + :param config: Configuration of service client. + :param serializer: An object model serializer. + :param deserializer: An object model deserializer. + """ + + models = _models + + def __init__(self, client, config, serializer, deserializer) -> None: + self._client = client + self._serialize = serializer + self._deserialize = deserializer + self._config = config + + @distributed_trace_async + async def get_schema_for_ml_table( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + body: Optional["_models.DataCallRequest"] = None, + **kwargs: Any + ) -> List["_models.ColumnDefinition"]: + """Get schema for a specific MLTable. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.DataCallRequest + :keyword callable cls: A custom type or function that will be passed the direct response + :return: list of ColumnDefinition, or the result of cls(response) + :rtype: list[~azure.mgmt.machinelearningservices.models.ColumnDefinition] + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType[List["_models.ColumnDefinition"]] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'DataCallRequest') + else: + _json = None + + request = build_get_schema_for_ml_table_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + content_type=content_type, + json=_json, + template_url=self.get_schema_for_ml_table.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('[ColumnDefinition]', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + get_schema_for_ml_table.metadata = {'url': '/data/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datacall/schema'} # type: ignore + + + @distributed_trace_async + async def get_preview_for_ml_table( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + body: Optional["_models.DataCallRequest"] = None, + **kwargs: Any + ) -> "_models.DataViewSetResult": + """Get preview for a specific MLTable. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.DataCallRequest + :keyword callable cls: A custom type or function that will be passed the direct response + :return: DataViewSetResult, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.DataViewSetResult + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.DataViewSetResult"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'DataCallRequest') + else: + _json = None + + request = build_get_preview_for_ml_table_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + content_type=content_type, + json=_json, + template_url=self.get_preview_for_ml_table.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('DataViewSetResult', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + get_preview_for_ml_table.metadata = {'url': '/data/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datacall/preview'} # type: ignore + + + @distributed_trace_async + async def get_quick_profile_for_ml_table( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + body: Optional["_models.DataCallRequest"] = None, + **kwargs: Any + ) -> List["_models.ProfileResult"]: + """Get quick profile for a specific MLTable. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.DataCallRequest + :keyword callable cls: A custom type or function that will be passed the direct response + :return: list of ProfileResult, or the result of cls(response) + :rtype: list[~azure.mgmt.machinelearningservices.models.ProfileResult] + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType[List["_models.ProfileResult"]] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'DataCallRequest') + else: + _json = None + + request = build_get_quick_profile_for_ml_table_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + content_type=content_type, + json=_json, + template_url=self.get_quick_profile_for_ml_table.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('[ProfileResult]', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + get_quick_profile_for_ml_table.metadata = {'url': '/data/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datacall/quickprofile'} # type: ignore + diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_data_container_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_data_container_operations.py new file mode 100644 index 00000000..c4c78abb --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_data_container_operations.py @@ -0,0 +1,321 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) AutoRest Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +import functools +from typing import Any, AsyncIterable, Callable, Dict, Generic, Optional, TypeVar +import warnings + +from azure.core.async_paging import AsyncItemPaged, AsyncList +from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error +from azure.core.pipeline import PipelineResponse +from azure.core.pipeline.transport import AsyncHttpResponse +from azure.core.rest import HttpRequest +from azure.core.tracing.decorator import distributed_trace +from azure.core.tracing.decorator_async import distributed_trace_async +from azure.mgmt.core.exceptions import ARMErrorFormat + +from ... import models as _models +from ..._vendor import _convert_request +from ...operations._data_container_operations import build_create_data_container_request, build_get_data_container_request, build_list_data_container_request, build_modify_data_container_request +T = TypeVar('T') +ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]] + +class DataContainerOperations: + """DataContainerOperations async operations. + + You should not instantiate this class directly. Instead, you should create a Client instance that + instantiates it for you and attaches it as an attribute. + + :ivar models: Alias to model classes used in this operation group. + :type models: ~azure.mgmt.machinelearningservices.models + :param client: Client for service requests. + :param config: Configuration of service client. + :param serializer: An object model serializer. + :param deserializer: An object model deserializer. + """ + + models = _models + + def __init__(self, client, config, serializer, deserializer) -> None: + self._client = client + self._serialize = serializer + self._deserialize = deserializer + self._config = config + + @distributed_trace_async + async def create_data_container( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + body: Optional["_models.DataContainer"] = None, + **kwargs: Any + ) -> "_models.DataContainerEntity": + """create_data_container. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.DataContainer + :keyword callable cls: A custom type or function that will be passed the direct response + :return: DataContainerEntity, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.DataContainerEntity + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.DataContainerEntity"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'DataContainer') + else: + _json = None + + request = build_create_data_container_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + content_type=content_type, + json=_json, + template_url=self.create_data_container.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('DataContainerEntity', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + create_data_container.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datacontainer'} # type: ignore + + + @distributed_trace + def list_data_container( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + **kwargs: Any + ) -> AsyncIterable["_models.PaginatedDataContainerEntityList"]: + """list_data_container. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :keyword callable cls: A custom type or function that will be passed the direct response + :return: An iterator like instance of either PaginatedDataContainerEntityList or the result of + cls(response) + :rtype: + ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.PaginatedDataContainerEntityList] + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.PaginatedDataContainerEntityList"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + def prepare_request(next_link=None): + if not next_link: + + request = build_list_data_container_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + template_url=self.list_data_container.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + else: + + request = build_list_data_container_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + template_url=next_link, + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + request.method = "GET" + return request + + async def extract_data(pipeline_response): + deserialized = self._deserialize("PaginatedDataContainerEntityList", pipeline_response) + list_of_elem = deserialized.value + if cls: + list_of_elem = cls(list_of_elem) + return deserialized.next_link or None, AsyncList(list_of_elem) + + async def get_next(next_link=None): + request = prepare_request(next_link) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + return pipeline_response + + + return AsyncItemPaged( + get_next, extract_data + ) + list_data_container.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datacontainer'} # type: ignore + + @distributed_trace_async + async def get_data_container( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + name: str, + **kwargs: Any + ) -> "_models.DataContainerEntity": + """get_data_container. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param name: + :type name: str + :keyword callable cls: A custom type or function that will be passed the direct response + :return: DataContainerEntity, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.DataContainerEntity + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.DataContainerEntity"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + + request = build_get_data_container_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + name=name, + template_url=self.get_data_container.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('DataContainerEntity', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + get_data_container.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datacontainer/{name}'} # type: ignore + + + @distributed_trace_async + async def modify_data_container( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + name: str, + body: Optional["_models.DataContainerMutable"] = None, + **kwargs: Any + ) -> "_models.DataContainerEntity": + """modify_data_container. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param name: + :type name: str + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.DataContainerMutable + :keyword callable cls: A custom type or function that will be passed the direct response + :return: DataContainerEntity, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.DataContainerEntity + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.DataContainerEntity"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'DataContainerMutable') + else: + _json = None + + request = build_modify_data_container_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + name=name, + content_type=content_type, + json=_json, + template_url=self.modify_data_container.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('DataContainerEntity', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + modify_data_container.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datacontainer/{name}'} # type: ignore + diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_data_version_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_data_version_operations.py new file mode 100644 index 00000000..9f375aeb --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_data_version_operations.py @@ -0,0 +1,804 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) AutoRest Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +import functools +from typing import Any, AsyncIterable, Callable, Dict, Generic, Optional, TypeVar +import warnings + +from azure.core.async_paging import AsyncItemPaged, AsyncList +from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error +from azure.core.pipeline import PipelineResponse +from azure.core.pipeline.transport import AsyncHttpResponse +from azure.core.rest import HttpRequest +from azure.core.tracing.decorator import distributed_trace +from azure.core.tracing.decorator_async import distributed_trace_async +from azure.mgmt.core.exceptions import ARMErrorFormat + +from ... import models as _models +from ..._vendor import _convert_request +from ...operations._data_version_operations import build_batch_get_resolved_uris_request, build_create_request, build_create_unregistered_input_data_request, build_create_unregistered_output_data_request, build_delete_request, build_exists_request, build_get_by_asset_id_request, build_get_request, build_list_request, build_modify_request, build_registered_existing_data_request +T = TypeVar('T') +ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]] + +class DataVersionOperations: + """DataVersionOperations async operations. + + You should not instantiate this class directly. Instead, you should create a Client instance that + instantiates it for you and attaches it as an attribute. + + :ivar models: Alias to model classes used in this operation group. + :type models: ~azure.mgmt.machinelearningservices.models + :param client: Client for service requests. + :param config: Configuration of service client. + :param serializer: An object model serializer. + :param deserializer: An object model deserializer. + """ + + models = _models + + def __init__(self, client, config, serializer, deserializer) -> None: + self._client = client + self._serialize = serializer + self._deserialize = deserializer + self._config = config + + @distributed_trace_async + async def create( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + name: str, + body: Optional["_models.DataVersion"] = None, + **kwargs: Any + ) -> "_models.DataVersionEntity": + """create. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param name: + :type name: str + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.DataVersion + :keyword callable cls: A custom type or function that will be passed the direct response + :return: DataVersionEntity, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.DataVersionEntity + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.DataVersionEntity"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'DataVersion') + else: + _json = None + + request = build_create_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + name=name, + content_type=content_type, + json=_json, + template_url=self.create.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('DataVersionEntity', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + create.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/{name}/versions'} # type: ignore + + + @distributed_trace + def list( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + name: str, + order_by: Optional[str] = None, + top: Optional[int] = None, + **kwargs: Any + ) -> AsyncIterable["_models.PaginatedDataVersionEntityList"]: + """list. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param name: + :type name: str + :param order_by: + :type order_by: str + :param top: + :type top: int + :keyword callable cls: A custom type or function that will be passed the direct response + :return: An iterator like instance of either PaginatedDataVersionEntityList or the result of + cls(response) + :rtype: + ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.PaginatedDataVersionEntityList] + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.PaginatedDataVersionEntityList"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + def prepare_request(next_link=None): + if not next_link: + + request = build_list_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + name=name, + order_by=order_by, + top=top, + template_url=self.list.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + else: + + request = build_list_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + name=name, + order_by=order_by, + top=top, + template_url=next_link, + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + request.method = "GET" + return request + + async def extract_data(pipeline_response): + deserialized = self._deserialize("PaginatedDataVersionEntityList", pipeline_response) + list_of_elem = deserialized.value + if cls: + list_of_elem = cls(list_of_elem) + return deserialized.next_link or None, AsyncList(list_of_elem) + + async def get_next(next_link=None): + request = prepare_request(next_link) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + return pipeline_response + + + return AsyncItemPaged( + get_next, extract_data + ) + list.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/{name}/versions'} # type: ignore + + @distributed_trace_async + async def get( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + name: str, + version: str, + **kwargs: Any + ) -> "_models.DataVersionEntity": + """get. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param name: + :type name: str + :param version: + :type version: str + :keyword callable cls: A custom type or function that will be passed the direct response + :return: DataVersionEntity, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.DataVersionEntity + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.DataVersionEntity"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + + request = build_get_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + name=name, + version=version, + template_url=self.get.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('DataVersionEntity', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + get.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/{name}/versions/{version}'} # type: ignore + + + @distributed_trace_async + async def modify( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + name: str, + version: str, + body: Optional["_models.DataVersionMutable"] = None, + **kwargs: Any + ) -> "_models.DataVersionEntity": + """modify. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param name: + :type name: str + :param version: + :type version: str + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.DataVersionMutable + :keyword callable cls: A custom type or function that will be passed the direct response + :return: DataVersionEntity, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.DataVersionEntity + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.DataVersionEntity"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'DataVersionMutable') + else: + _json = None + + request = build_modify_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + name=name, + version=version, + content_type=content_type, + json=_json, + template_url=self.modify.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('DataVersionEntity', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + modify.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/{name}/versions/{version}'} # type: ignore + + + @distributed_trace_async + async def delete( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + name: str, + version: str, + **kwargs: Any + ) -> "_models.HttpResponseMessage": + """delete. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param name: + :type name: str + :param version: + :type version: str + :keyword callable cls: A custom type or function that will be passed the direct response + :return: HttpResponseMessage, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.HttpResponseMessage + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.HttpResponseMessage"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + + request = build_delete_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + name=name, + version=version, + template_url=self.delete.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('HttpResponseMessage', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + delete.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/{name}/versions/{version}'} # type: ignore + + + @distributed_trace_async + async def exists( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + name: str, + version: str, + **kwargs: Any + ) -> bool: + """exists. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param name: + :type name: str + :param version: + :type version: str + :keyword callable cls: A custom type or function that will be passed the direct response + :return: bool, or the result of cls(response) + :rtype: bool + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType[bool] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + + request = build_exists_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + name=name, + version=version, + template_url=self.exists.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('bool', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + exists.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/{name}/versions/{version}/exists'} # type: ignore + + + @distributed_trace_async + async def get_by_asset_id( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + body: Optional["_models.AssetId"] = None, + **kwargs: Any + ) -> "_models.DataVersionEntity": + """get_by_asset_id. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.AssetId + :keyword callable cls: A custom type or function that will be passed the direct response + :return: DataVersionEntity, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.DataVersionEntity + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.DataVersionEntity"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'AssetId') + else: + _json = None + + request = build_get_by_asset_id_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + content_type=content_type, + json=_json, + template_url=self.get_by_asset_id.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('DataVersionEntity', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + get_by_asset_id.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/getByAssetId'} # type: ignore + + + @distributed_trace_async + async def create_unregistered_input_data( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + body: Optional["_models.CreateUnregisteredInputData"] = None, + **kwargs: Any + ) -> "_models.DataContainerEntity": + """create_unregistered_input_data. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.CreateUnregisteredInputData + :keyword callable cls: A custom type or function that will be passed the direct response + :return: DataContainerEntity, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.DataContainerEntity + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.DataContainerEntity"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'CreateUnregisteredInputData') + else: + _json = None + + request = build_create_unregistered_input_data_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + content_type=content_type, + json=_json, + template_url=self.create_unregistered_input_data.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('DataContainerEntity', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + create_unregistered_input_data.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/createUnregisteredInput'} # type: ignore + + + @distributed_trace_async + async def create_unregistered_output_data( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + body: Optional["_models.CreateUnregisteredOutputData"] = None, + **kwargs: Any + ) -> "_models.DataContainerEntity": + """create_unregistered_output_data. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.CreateUnregisteredOutputData + :keyword callable cls: A custom type or function that will be passed the direct response + :return: DataContainerEntity, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.DataContainerEntity + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.DataContainerEntity"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'CreateUnregisteredOutputData') + else: + _json = None + + request = build_create_unregistered_output_data_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + content_type=content_type, + json=_json, + template_url=self.create_unregistered_output_data.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('DataContainerEntity', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + create_unregistered_output_data.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/createUnregisteredOutput'} # type: ignore + + + @distributed_trace_async + async def registered_existing_data( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + body: Optional["_models.RegisterExistingData"] = None, + **kwargs: Any + ) -> "_models.DataContainerEntity": + """registered_existing_data. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.RegisterExistingData + :keyword callable cls: A custom type or function that will be passed the direct response + :return: DataContainerEntity, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.DataContainerEntity + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.DataContainerEntity"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'RegisterExistingData') + else: + _json = None + + request = build_registered_existing_data_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + content_type=content_type, + json=_json, + template_url=self.registered_existing_data.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('DataContainerEntity', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + registered_existing_data.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/registerExisting'} # type: ignore + + + @distributed_trace_async + async def batch_get_resolved_uris( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + body: Optional["_models.BatchGetResolvedURIs"] = None, + **kwargs: Any + ) -> "_models.BatchDataUriResponse": + """batch_get_resolved_uris. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.BatchGetResolvedURIs + :keyword callable cls: A custom type or function that will be passed the direct response + :return: BatchDataUriResponse, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.BatchDataUriResponse + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.BatchDataUriResponse"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'BatchGetResolvedURIs') + else: + _json = None + + request = build_batch_get_resolved_uris_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + content_type=content_type, + json=_json, + template_url=self.batch_get_resolved_uris.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('BatchDataUriResponse', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + batch_get_resolved_uris.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/batchGetResolvedUris'} # type: ignore + diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_dataset_controller_v2_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_dataset_controller_v2_operations.py new file mode 100644 index 00000000..a3574be3 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_dataset_controller_v2_operations.py @@ -0,0 +1,845 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) AutoRest Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +import functools +from typing import Any, AsyncIterable, Callable, Dict, Generic, List, Optional, TypeVar +import warnings + +from azure.core.async_paging import AsyncItemPaged, AsyncList +from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error +from azure.core.pipeline import PipelineResponse +from azure.core.pipeline.transport import AsyncHttpResponse +from azure.core.rest import HttpRequest +from azure.core.tracing.decorator import distributed_trace +from azure.core.tracing.decorator_async import distributed_trace_async +from azure.mgmt.core.exceptions import ARMErrorFormat + +from ... import models as _models +from ..._vendor import _convert_request +from ...operations._dataset_controller_v2_operations import build_delete_all_datasets_request, build_get_all_dataset_definitions_request, build_get_all_dataset_versions_request, build_get_dataset_by_name_request, build_get_dataset_definition_request, build_list_request, build_register_request, build_unregister_dataset_request, build_update_dataset_request, build_update_definition_request +T = TypeVar('T') +ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]] + +class DatasetControllerV2Operations: + """DatasetControllerV2Operations async operations. + + You should not instantiate this class directly. Instead, you should create a Client instance that + instantiates it for you and attaches it as an attribute. + + :ivar models: Alias to model classes used in this operation group. + :type models: ~azure.mgmt.machinelearningservices.models + :param client: Client for service requests. + :param config: Configuration of service client. + :param serializer: An object model serializer. + :param deserializer: An object model deserializer. + """ + + models = _models + + def __init__(self, client, config, serializer, deserializer) -> None: + self._client = client + self._serialize = serializer + self._deserialize = deserializer + self._config = config + + @distributed_trace_async + async def get_dataset_definition( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + dataset_id: str, + version: str, + **kwargs: Any + ) -> "_models.DatasetDefinition": + """Get a specific dataset definition. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param dataset_id: + :type dataset_id: str + :param version: + :type version: str + :keyword callable cls: A custom type or function that will be passed the direct response + :return: DatasetDefinition, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.DatasetDefinition + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.DatasetDefinition"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + + request = build_get_dataset_definition_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_id=dataset_id, + version=version, + template_url=self.get_dataset_definition.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('DatasetDefinition', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + get_dataset_definition.metadata = {'url': '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}/definitions/{version}'} # type: ignore + + + @distributed_trace + def get_all_dataset_definitions( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + dataset_id: str, + continuation_token_parameter: Optional[str] = None, + page_size: Optional[int] = None, + **kwargs: Any + ) -> AsyncIterable["_models.PaginatedDatasetDefinitionList"]: + """Get all dataset definitions for a given dataset. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param dataset_id: + :type dataset_id: str + :param continuation_token_parameter: + :type continuation_token_parameter: str + :param page_size: + :type page_size: int + :keyword callable cls: A custom type or function that will be passed the direct response + :return: An iterator like instance of either PaginatedDatasetDefinitionList or the result of + cls(response) + :rtype: + ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.PaginatedDatasetDefinitionList] + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.PaginatedDatasetDefinitionList"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + def prepare_request(next_link=None): + if not next_link: + + request = build_get_all_dataset_definitions_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_id=dataset_id, + continuation_token_parameter=continuation_token_parameter, + page_size=page_size, + template_url=self.get_all_dataset_definitions.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + else: + + request = build_get_all_dataset_definitions_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_id=dataset_id, + continuation_token_parameter=continuation_token_parameter, + page_size=page_size, + template_url=next_link, + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + request.method = "GET" + return request + + async def extract_data(pipeline_response): + deserialized = self._deserialize("PaginatedDatasetDefinitionList", pipeline_response) + list_of_elem = deserialized.value + if cls: + list_of_elem = cls(list_of_elem) + return deserialized.next_link or None, AsyncList(list_of_elem) + + async def get_next(next_link=None): + request = prepare_request(next_link) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + return pipeline_response + + + return AsyncItemPaged( + get_next, extract_data + ) + get_all_dataset_definitions.metadata = {'url': '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}/definitions'} # type: ignore + + @distributed_trace_async + async def update_definition( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + dataset_id: str, + register_as_pending: Optional[bool] = False, + force_update: Optional[bool] = False, + dataset_type: Optional[str] = None, + user_version_id: Optional[str] = None, + body: Optional["_models.DatasetDefinition"] = None, + **kwargs: Any + ) -> "_models.Dataset": + """Update a dataset definition. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param dataset_id: + :type dataset_id: str + :param register_as_pending: + :type register_as_pending: bool + :param force_update: + :type force_update: bool + :param dataset_type: + :type dataset_type: str + :param user_version_id: + :type user_version_id: str + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.DatasetDefinition + :keyword callable cls: A custom type or function that will be passed the direct response + :return: Dataset, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.Dataset + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.Dataset"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'DatasetDefinition') + else: + _json = None + + request = build_update_definition_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_id=dataset_id, + content_type=content_type, + json=_json, + register_as_pending=register_as_pending, + force_update=force_update, + dataset_type=dataset_type, + user_version_id=user_version_id, + template_url=self.update_definition.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('Dataset', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + update_definition.metadata = {'url': '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}/definitions'} # type: ignore + + + @distributed_trace + def get_all_dataset_versions( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + dataset_id: str, + continuation_token_parameter: Optional[str] = None, + page_size: Optional[int] = None, + **kwargs: Any + ) -> AsyncIterable["_models.PaginatedStringList"]: + """Get all dataset versions for a given dataset. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param dataset_id: + :type dataset_id: str + :param continuation_token_parameter: + :type continuation_token_parameter: str + :param page_size: + :type page_size: int + :keyword callable cls: A custom type or function that will be passed the direct response + :return: An iterator like instance of either PaginatedStringList or the result of cls(response) + :rtype: + ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.PaginatedStringList] + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.PaginatedStringList"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + def prepare_request(next_link=None): + if not next_link: + + request = build_get_all_dataset_versions_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_id=dataset_id, + continuation_token_parameter=continuation_token_parameter, + page_size=page_size, + template_url=self.get_all_dataset_versions.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + else: + + request = build_get_all_dataset_versions_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_id=dataset_id, + continuation_token_parameter=continuation_token_parameter, + page_size=page_size, + template_url=next_link, + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + request.method = "GET" + return request + + async def extract_data(pipeline_response): + deserialized = self._deserialize("PaginatedStringList", pipeline_response) + list_of_elem = deserialized.value + if cls: + list_of_elem = cls(list_of_elem) + return deserialized.next_link or None, AsyncList(list_of_elem) + + async def get_next(next_link=None): + request = prepare_request(next_link) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + return pipeline_response + + + return AsyncItemPaged( + get_next, extract_data + ) + get_all_dataset_versions.metadata = {'url': '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}/versions'} # type: ignore + + @distributed_trace_async + async def get_dataset_by_name( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + dataset_name: str, + version_id: Optional[str] = None, + include_latest_definition: Optional[bool] = True, + **kwargs: Any + ) -> "_models.Dataset": + """Get a dataset for a given dataset name. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param dataset_name: + :type dataset_name: str + :param version_id: + :type version_id: str + :param include_latest_definition: + :type include_latest_definition: bool + :keyword callable cls: A custom type or function that will be passed the direct response + :return: Dataset, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.Dataset + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.Dataset"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + + request = build_get_dataset_by_name_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_name=dataset_name, + version_id=version_id, + include_latest_definition=include_latest_definition, + template_url=self.get_dataset_by_name.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('Dataset', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + get_dataset_by_name.metadata = {'url': '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/query/name={datasetName}'} # type: ignore + + + @distributed_trace + def list( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + dataset_names: Optional[List[str]] = None, + search_text: Optional[str] = None, + include_invisible: Optional[bool] = False, + status: Optional[str] = None, + continuation_token_parameter: Optional[str] = None, + page_size: Optional[int] = None, + include_latest_definition: Optional[bool] = False, + order_by: Optional[str] = None, + order_by_asc: Optional[bool] = False, + dataset_types: Optional[List[str]] = None, + **kwargs: Any + ) -> AsyncIterable["_models.PaginatedDatasetList"]: + """Get a list of datasets. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param dataset_names: + :type dataset_names: list[str] + :param search_text: + :type search_text: str + :param include_invisible: + :type include_invisible: bool + :param status: + :type status: str + :param continuation_token_parameter: + :type continuation_token_parameter: str + :param page_size: + :type page_size: int + :param include_latest_definition: + :type include_latest_definition: bool + :param order_by: + :type order_by: str + :param order_by_asc: + :type order_by_asc: bool + :param dataset_types: + :type dataset_types: list[str] + :keyword callable cls: A custom type or function that will be passed the direct response + :return: An iterator like instance of either PaginatedDatasetList or the result of + cls(response) + :rtype: + ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.PaginatedDatasetList] + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.PaginatedDatasetList"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + def prepare_request(next_link=None): + if not next_link: + + request = build_list_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_names=dataset_names, + search_text=search_text, + include_invisible=include_invisible, + status=status, + continuation_token_parameter=continuation_token_parameter, + page_size=page_size, + include_latest_definition=include_latest_definition, + order_by=order_by, + order_by_asc=order_by_asc, + dataset_types=dataset_types, + template_url=self.list.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + else: + + request = build_list_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_names=dataset_names, + search_text=search_text, + include_invisible=include_invisible, + status=status, + continuation_token_parameter=continuation_token_parameter, + page_size=page_size, + include_latest_definition=include_latest_definition, + order_by=order_by, + order_by_asc=order_by_asc, + dataset_types=dataset_types, + template_url=next_link, + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + request.method = "GET" + return request + + async def extract_data(pipeline_response): + deserialized = self._deserialize("PaginatedDatasetList", pipeline_response) + list_of_elem = deserialized.value + if cls: + list_of_elem = cls(list_of_elem) + return deserialized.next_link or None, AsyncList(list_of_elem) + + async def get_next(next_link=None): + request = prepare_request(next_link) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + return pipeline_response + + + return AsyncItemPaged( + get_next, extract_data + ) + list.metadata = {'url': '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets'} # type: ignore + + @distributed_trace_async + async def register( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + register_as_pending: Optional[bool] = False, + if_exists_ok: Optional[bool] = True, + update_definition_if_exists: Optional[bool] = False, + with_data_hash: Optional[bool] = False, + user_version_id: Optional[str] = None, + body: Optional["_models.Dataset"] = None, + **kwargs: Any + ) -> "_models.Dataset": + """Register new dataset. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param register_as_pending: + :type register_as_pending: bool + :param if_exists_ok: + :type if_exists_ok: bool + :param update_definition_if_exists: + :type update_definition_if_exists: bool + :param with_data_hash: + :type with_data_hash: bool + :param user_version_id: + :type user_version_id: str + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.Dataset + :keyword callable cls: A custom type or function that will be passed the direct response + :return: Dataset, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.Dataset + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.Dataset"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'Dataset') + else: + _json = None + + request = build_register_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + content_type=content_type, + json=_json, + register_as_pending=register_as_pending, + if_exists_ok=if_exists_ok, + update_definition_if_exists=update_definition_if_exists, + with_data_hash=with_data_hash, + user_version_id=user_version_id, + template_url=self.register.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('Dataset', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + register.metadata = {'url': '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets'} # type: ignore + + + @distributed_trace_async + async def delete_all_datasets( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + **kwargs: Any + ) -> None: + """Unregister all datasets in the workspace. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :keyword callable cls: A custom type or function that will be passed the direct response + :return: None, or the result of cls(response) + :rtype: None + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType[None] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + + request = build_delete_all_datasets_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + template_url=self.delete_all_datasets.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in []: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + if cls: + return cls(pipeline_response, None, {}) + + delete_all_datasets.metadata = {'url': '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets'} # type: ignore + + + @distributed_trace_async + async def update_dataset( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + dataset_id: str, + force_update: Optional[bool] = False, + body: Optional["_models.Dataset"] = None, + **kwargs: Any + ) -> "_models.Dataset": + """Update a dataset. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param dataset_id: + :type dataset_id: str + :param force_update: + :type force_update: bool + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.Dataset + :keyword callable cls: A custom type or function that will be passed the direct response + :return: Dataset, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.Dataset + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.Dataset"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'Dataset') + else: + _json = None + + request = build_update_dataset_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_id=dataset_id, + content_type=content_type, + json=_json, + force_update=force_update, + template_url=self.update_dataset.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('Dataset', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + update_dataset.metadata = {'url': '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}'} # type: ignore + + + @distributed_trace_async + async def unregister_dataset( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + name: str, + **kwargs: Any + ) -> None: + """Unregister a dataset. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param name: + :type name: str + :keyword callable cls: A custom type or function that will be passed the direct response + :return: None, or the result of cls(response) + :rtype: None + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType[None] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + + request = build_unregister_dataset_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + name=name, + template_url=self.unregister_dataset.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in []: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + if cls: + return cls(pipeline_response, None, {}) + + unregister_dataset.metadata = {'url': '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{name}'} # type: ignore + diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_dataset_v2_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_dataset_v2_operations.py new file mode 100644 index 00000000..584122c1 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_dataset_v2_operations.py @@ -0,0 +1,592 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) AutoRest Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +import functools +from typing import Any, AsyncIterable, Callable, Dict, Generic, List, Optional, TypeVar +import warnings + +from azure.core.async_paging import AsyncItemPaged, AsyncList +from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error +from azure.core.pipeline import PipelineResponse +from azure.core.pipeline.transport import AsyncHttpResponse +from azure.core.rest import HttpRequest +from azure.core.tracing.decorator import distributed_trace +from azure.core.tracing.decorator_async import distributed_trace_async +from azure.mgmt.core.exceptions import ARMErrorFormat + +from ... import models as _models +from ..._vendor import _convert_request +from ...operations._dataset_v2_operations import build_create_request, build_delete_all_datasets_request, build_delete_dataset_by_name_request, build_get_dataset_by_id_request, build_get_dataset_by_name_request, build_list_request, build_update_dataset_by_name_and_version_request, build_update_dataset_request +T = TypeVar('T') +ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]] + +class DatasetV2Operations: + """DatasetV2Operations async operations. + + You should not instantiate this class directly. Instead, you should create a Client instance that + instantiates it for you and attaches it as an attribute. + + :ivar models: Alias to model classes used in this operation group. + :type models: ~azure.mgmt.machinelearningservices.models + :param client: Client for service requests. + :param config: Configuration of service client. + :param serializer: An object model serializer. + :param deserializer: An object model deserializer. + """ + + models = _models + + def __init__(self, client, config, serializer, deserializer) -> None: + self._client = client + self._serialize = serializer + self._deserialize = deserializer + self._config = config + + @distributed_trace_async + async def create( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + if_exists_update: Optional[bool] = False, + body: Optional["_models.DatasetV2"] = None, + **kwargs: Any + ) -> "_models.DatasetV2": + """Create new dataset. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param if_exists_update: + :type if_exists_update: bool + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.DatasetV2 + :keyword callable cls: A custom type or function that will be passed the direct response + :return: DatasetV2, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.DatasetV2 + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.DatasetV2"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'DatasetV2') + else: + _json = None + + request = build_create_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + content_type=content_type, + json=_json, + if_exists_update=if_exists_update, + template_url=self.create.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('DatasetV2', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + create.metadata = {'url': '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets'} # type: ignore + + + @distributed_trace_async + async def delete_all_datasets( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + **kwargs: Any + ) -> None: + """Delete all datasets in the workspace. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :keyword callable cls: A custom type or function that will be passed the direct response + :return: None, or the result of cls(response) + :rtype: None + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType[None] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + + request = build_delete_all_datasets_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + template_url=self.delete_all_datasets.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in []: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + if cls: + return cls(pipeline_response, None, {}) + + delete_all_datasets.metadata = {'url': '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets'} # type: ignore + + + @distributed_trace + def list( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + names: Optional[List[str]] = None, + search_text: Optional[str] = None, + continuation_token_parameter: Optional[str] = None, + page_size: Optional[int] = None, + **kwargs: Any + ) -> AsyncIterable["_models.PaginatedDatasetV2List"]: + """Get a list of datasets. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param names: + :type names: list[str] + :param search_text: + :type search_text: str + :param continuation_token_parameter: + :type continuation_token_parameter: str + :param page_size: + :type page_size: int + :keyword callable cls: A custom type or function that will be passed the direct response + :return: An iterator like instance of either PaginatedDatasetV2List or the result of + cls(response) + :rtype: + ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.PaginatedDatasetV2List] + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.PaginatedDatasetV2List"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + def prepare_request(next_link=None): + if not next_link: + + request = build_list_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + names=names, + search_text=search_text, + continuation_token_parameter=continuation_token_parameter, + page_size=page_size, + template_url=self.list.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + else: + + request = build_list_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + names=names, + search_text=search_text, + continuation_token_parameter=continuation_token_parameter, + page_size=page_size, + template_url=next_link, + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + request.method = "GET" + return request + + async def extract_data(pipeline_response): + deserialized = self._deserialize("PaginatedDatasetV2List", pipeline_response) + list_of_elem = deserialized.value + if cls: + list_of_elem = cls(list_of_elem) + return deserialized.next_link or None, AsyncList(list_of_elem) + + async def get_next(next_link=None): + request = prepare_request(next_link) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + return pipeline_response + + + return AsyncItemPaged( + get_next, extract_data + ) + list.metadata = {'url': '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets'} # type: ignore + + @distributed_trace_async + async def delete_dataset_by_name( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + name: str, + version_id: str, + **kwargs: Any + ) -> None: + """Delete a dataset. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param name: + :type name: str + :param version_id: + :type version_id: str + :keyword callable cls: A custom type or function that will be passed the direct response + :return: None, or the result of cls(response) + :rtype: None + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType[None] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + + request = build_delete_dataset_by_name_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + name=name, + version_id=version_id, + template_url=self.delete_dataset_by_name.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in []: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + if cls: + return cls(pipeline_response, None, {}) + + delete_dataset_by_name.metadata = {'url': '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{name}/versions/{versionId}'} # type: ignore + + + @distributed_trace_async + async def update_dataset_by_name_and_version( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + name: str, + version_id: str, + body: Optional["_models.DatasetV2"] = None, + **kwargs: Any + ) -> "_models.DatasetV2": + """Update a dataset by its name and version. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param name: + :type name: str + :param version_id: + :type version_id: str + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.DatasetV2 + :keyword callable cls: A custom type or function that will be passed the direct response + :return: DatasetV2, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.DatasetV2 + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.DatasetV2"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'DatasetV2') + else: + _json = None + + request = build_update_dataset_by_name_and_version_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + name=name, + version_id=version_id, + content_type=content_type, + json=_json, + template_url=self.update_dataset_by_name_and_version.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('DatasetV2', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + update_dataset_by_name_and_version.metadata = {'url': '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{name}/versions/{versionId}'} # type: ignore + + + @distributed_trace_async + async def get_dataset_by_id( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + dataset_id: str, + **kwargs: Any + ) -> "_models.DatasetV2": + """Get a dataset for a given dataset id. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param dataset_id: + :type dataset_id: str + :keyword callable cls: A custom type or function that will be passed the direct response + :return: DatasetV2, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.DatasetV2 + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.DatasetV2"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + + request = build_get_dataset_by_id_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_id=dataset_id, + template_url=self.get_dataset_by_id.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('DatasetV2', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + get_dataset_by_id.metadata = {'url': '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}'} # type: ignore + + + @distributed_trace_async + async def update_dataset( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + dataset_id: str, + body: Optional["_models.DatasetV2"] = None, + **kwargs: Any + ) -> "_models.DatasetV2": + """Update a dataset. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param dataset_id: + :type dataset_id: str + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.DatasetV2 + :keyword callable cls: A custom type or function that will be passed the direct response + :return: DatasetV2, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.DatasetV2 + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.DatasetV2"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'DatasetV2') + else: + _json = None + + request = build_update_dataset_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_id=dataset_id, + content_type=content_type, + json=_json, + template_url=self.update_dataset.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('DatasetV2', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + update_dataset.metadata = {'url': '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}'} # type: ignore + + + @distributed_trace_async + async def get_dataset_by_name( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + dataset_name: str, + version_id: Optional[str] = None, + **kwargs: Any + ) -> "_models.DatasetV2": + """Get a dataset for a given dataset name. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param dataset_name: + :type dataset_name: str + :param version_id: + :type version_id: str + :keyword callable cls: A custom type or function that will be passed the direct response + :return: DatasetV2, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.DatasetV2 + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.DatasetV2"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + + request = build_get_dataset_by_name_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_name=dataset_name, + version_id=version_id, + template_url=self.get_dataset_by_name.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('DatasetV2', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + get_dataset_by_name.metadata = {'url': '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/query/name={datasetName}'} # type: ignore + diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_datasets_v1_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_datasets_v1_operations.py new file mode 100644 index 00000000..97e74d53 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_datasets_v1_operations.py @@ -0,0 +1,845 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) AutoRest Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +import functools +from typing import Any, AsyncIterable, Callable, Dict, Generic, List, Optional, TypeVar +import warnings + +from azure.core.async_paging import AsyncItemPaged, AsyncList +from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error +from azure.core.pipeline import PipelineResponse +from azure.core.pipeline.transport import AsyncHttpResponse +from azure.core.rest import HttpRequest +from azure.core.tracing.decorator import distributed_trace +from azure.core.tracing.decorator_async import distributed_trace_async +from azure.mgmt.core.exceptions import ARMErrorFormat + +from ... import models as _models +from ..._vendor import _convert_request +from ...operations._datasets_v1_operations import build_delete_all_datasets_request, build_get_all_dataset_definitions_request, build_get_all_dataset_versions_request, build_get_dataset_by_name_request, build_get_dataset_definition_request, build_list_request, build_register_request, build_unregister_dataset_request, build_update_dataset_request, build_update_definition_request +T = TypeVar('T') +ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]] + +class DatasetsV1Operations: + """DatasetsV1Operations async operations. + + You should not instantiate this class directly. Instead, you should create a Client instance that + instantiates it for you and attaches it as an attribute. + + :ivar models: Alias to model classes used in this operation group. + :type models: ~azure.mgmt.machinelearningservices.models + :param client: Client for service requests. + :param config: Configuration of service client. + :param serializer: An object model serializer. + :param deserializer: An object model deserializer. + """ + + models = _models + + def __init__(self, client, config, serializer, deserializer) -> None: + self._client = client + self._serialize = serializer + self._deserialize = deserializer + self._config = config + + @distributed_trace_async + async def get_dataset_definition( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + dataset_id: str, + version: str, + **kwargs: Any + ) -> "_models.DatasetDefinition": + """Get a specific dataset definition. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param dataset_id: + :type dataset_id: str + :param version: + :type version: str + :keyword callable cls: A custom type or function that will be passed the direct response + :return: DatasetDefinition, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.DatasetDefinition + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.DatasetDefinition"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + + request = build_get_dataset_definition_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_id=dataset_id, + version=version, + template_url=self.get_dataset_definition.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('DatasetDefinition', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + get_dataset_definition.metadata = {'url': '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}/definitions/{version}'} # type: ignore + + + @distributed_trace + def get_all_dataset_definitions( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + dataset_id: str, + continuation_token_parameter: Optional[str] = None, + page_size: Optional[int] = None, + **kwargs: Any + ) -> AsyncIterable["_models.PaginatedDatasetDefinitionList"]: + """Get all dataset definitions for a given dataset. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param dataset_id: + :type dataset_id: str + :param continuation_token_parameter: + :type continuation_token_parameter: str + :param page_size: + :type page_size: int + :keyword callable cls: A custom type or function that will be passed the direct response + :return: An iterator like instance of either PaginatedDatasetDefinitionList or the result of + cls(response) + :rtype: + ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.PaginatedDatasetDefinitionList] + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.PaginatedDatasetDefinitionList"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + def prepare_request(next_link=None): + if not next_link: + + request = build_get_all_dataset_definitions_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_id=dataset_id, + continuation_token_parameter=continuation_token_parameter, + page_size=page_size, + template_url=self.get_all_dataset_definitions.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + else: + + request = build_get_all_dataset_definitions_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_id=dataset_id, + continuation_token_parameter=continuation_token_parameter, + page_size=page_size, + template_url=next_link, + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + request.method = "GET" + return request + + async def extract_data(pipeline_response): + deserialized = self._deserialize("PaginatedDatasetDefinitionList", pipeline_response) + list_of_elem = deserialized.value + if cls: + list_of_elem = cls(list_of_elem) + return deserialized.next_link or None, AsyncList(list_of_elem) + + async def get_next(next_link=None): + request = prepare_request(next_link) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + return pipeline_response + + + return AsyncItemPaged( + get_next, extract_data + ) + get_all_dataset_definitions.metadata = {'url': '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}/definitions'} # type: ignore + + @distributed_trace_async + async def update_definition( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + dataset_id: str, + register_as_pending: Optional[bool] = False, + force_update: Optional[bool] = False, + dataset_type: Optional[str] = None, + user_version_id: Optional[str] = None, + body: Optional["_models.DatasetDefinition"] = None, + **kwargs: Any + ) -> "_models.Dataset": + """Update a dataset definition. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param dataset_id: + :type dataset_id: str + :param register_as_pending: + :type register_as_pending: bool + :param force_update: + :type force_update: bool + :param dataset_type: + :type dataset_type: str + :param user_version_id: + :type user_version_id: str + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.DatasetDefinition + :keyword callable cls: A custom type or function that will be passed the direct response + :return: Dataset, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.Dataset + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.Dataset"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'DatasetDefinition') + else: + _json = None + + request = build_update_definition_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_id=dataset_id, + content_type=content_type, + json=_json, + register_as_pending=register_as_pending, + force_update=force_update, + dataset_type=dataset_type, + user_version_id=user_version_id, + template_url=self.update_definition.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('Dataset', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + update_definition.metadata = {'url': '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}/definitions'} # type: ignore + + + @distributed_trace + def get_all_dataset_versions( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + dataset_id: str, + continuation_token_parameter: Optional[str] = None, + page_size: Optional[int] = None, + **kwargs: Any + ) -> AsyncIterable["_models.PaginatedStringList"]: + """Get all dataset versions for a given dataset. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param dataset_id: + :type dataset_id: str + :param continuation_token_parameter: + :type continuation_token_parameter: str + :param page_size: + :type page_size: int + :keyword callable cls: A custom type or function that will be passed the direct response + :return: An iterator like instance of either PaginatedStringList or the result of cls(response) + :rtype: + ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.PaginatedStringList] + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.PaginatedStringList"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + def prepare_request(next_link=None): + if not next_link: + + request = build_get_all_dataset_versions_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_id=dataset_id, + continuation_token_parameter=continuation_token_parameter, + page_size=page_size, + template_url=self.get_all_dataset_versions.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + else: + + request = build_get_all_dataset_versions_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_id=dataset_id, + continuation_token_parameter=continuation_token_parameter, + page_size=page_size, + template_url=next_link, + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + request.method = "GET" + return request + + async def extract_data(pipeline_response): + deserialized = self._deserialize("PaginatedStringList", pipeline_response) + list_of_elem = deserialized.value + if cls: + list_of_elem = cls(list_of_elem) + return deserialized.next_link or None, AsyncList(list_of_elem) + + async def get_next(next_link=None): + request = prepare_request(next_link) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + return pipeline_response + + + return AsyncItemPaged( + get_next, extract_data + ) + get_all_dataset_versions.metadata = {'url': '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}/versions'} # type: ignore + + @distributed_trace_async + async def get_dataset_by_name( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + dataset_name: str, + version_id: Optional[str] = None, + include_latest_definition: Optional[bool] = True, + **kwargs: Any + ) -> "_models.Dataset": + """Get a dataset for a given dataset name. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param dataset_name: + :type dataset_name: str + :param version_id: + :type version_id: str + :param include_latest_definition: + :type include_latest_definition: bool + :keyword callable cls: A custom type or function that will be passed the direct response + :return: Dataset, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.Dataset + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.Dataset"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + + request = build_get_dataset_by_name_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_name=dataset_name, + version_id=version_id, + include_latest_definition=include_latest_definition, + template_url=self.get_dataset_by_name.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('Dataset', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + get_dataset_by_name.metadata = {'url': '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/query/name={datasetName}'} # type: ignore + + + @distributed_trace + def list( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + dataset_names: Optional[List[str]] = None, + search_text: Optional[str] = None, + include_invisible: Optional[bool] = False, + status: Optional[str] = None, + continuation_token_parameter: Optional[str] = None, + page_size: Optional[int] = None, + include_latest_definition: Optional[bool] = False, + order_by: Optional[str] = None, + order_by_asc: Optional[bool] = False, + dataset_types: Optional[List[str]] = None, + **kwargs: Any + ) -> AsyncIterable["_models.PaginatedDatasetList"]: + """Get a list of datasets. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param dataset_names: + :type dataset_names: list[str] + :param search_text: + :type search_text: str + :param include_invisible: + :type include_invisible: bool + :param status: + :type status: str + :param continuation_token_parameter: + :type continuation_token_parameter: str + :param page_size: + :type page_size: int + :param include_latest_definition: + :type include_latest_definition: bool + :param order_by: + :type order_by: str + :param order_by_asc: + :type order_by_asc: bool + :param dataset_types: + :type dataset_types: list[str] + :keyword callable cls: A custom type or function that will be passed the direct response + :return: An iterator like instance of either PaginatedDatasetList or the result of + cls(response) + :rtype: + ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.PaginatedDatasetList] + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.PaginatedDatasetList"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + def prepare_request(next_link=None): + if not next_link: + + request = build_list_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_names=dataset_names, + search_text=search_text, + include_invisible=include_invisible, + status=status, + continuation_token_parameter=continuation_token_parameter, + page_size=page_size, + include_latest_definition=include_latest_definition, + order_by=order_by, + order_by_asc=order_by_asc, + dataset_types=dataset_types, + template_url=self.list.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + else: + + request = build_list_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_names=dataset_names, + search_text=search_text, + include_invisible=include_invisible, + status=status, + continuation_token_parameter=continuation_token_parameter, + page_size=page_size, + include_latest_definition=include_latest_definition, + order_by=order_by, + order_by_asc=order_by_asc, + dataset_types=dataset_types, + template_url=next_link, + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + request.method = "GET" + return request + + async def extract_data(pipeline_response): + deserialized = self._deserialize("PaginatedDatasetList", pipeline_response) + list_of_elem = deserialized.value + if cls: + list_of_elem = cls(list_of_elem) + return deserialized.next_link or None, AsyncList(list_of_elem) + + async def get_next(next_link=None): + request = prepare_request(next_link) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + return pipeline_response + + + return AsyncItemPaged( + get_next, extract_data + ) + list.metadata = {'url': '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets'} # type: ignore + + @distributed_trace_async + async def register( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + register_as_pending: Optional[bool] = False, + if_exists_ok: Optional[bool] = True, + update_definition_if_exists: Optional[bool] = False, + with_data_hash: Optional[bool] = False, + user_version_id: Optional[str] = None, + body: Optional["_models.Dataset"] = None, + **kwargs: Any + ) -> "_models.Dataset": + """Register new dataset. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param register_as_pending: + :type register_as_pending: bool + :param if_exists_ok: + :type if_exists_ok: bool + :param update_definition_if_exists: + :type update_definition_if_exists: bool + :param with_data_hash: + :type with_data_hash: bool + :param user_version_id: + :type user_version_id: str + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.Dataset + :keyword callable cls: A custom type or function that will be passed the direct response + :return: Dataset, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.Dataset + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.Dataset"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'Dataset') + else: + _json = None + + request = build_register_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + content_type=content_type, + json=_json, + register_as_pending=register_as_pending, + if_exists_ok=if_exists_ok, + update_definition_if_exists=update_definition_if_exists, + with_data_hash=with_data_hash, + user_version_id=user_version_id, + template_url=self.register.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('Dataset', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + register.metadata = {'url': '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets'} # type: ignore + + + @distributed_trace_async + async def delete_all_datasets( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + **kwargs: Any + ) -> None: + """Unregister all datasets in the workspace. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :keyword callable cls: A custom type or function that will be passed the direct response + :return: None, or the result of cls(response) + :rtype: None + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType[None] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + + request = build_delete_all_datasets_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + template_url=self.delete_all_datasets.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in []: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + if cls: + return cls(pipeline_response, None, {}) + + delete_all_datasets.metadata = {'url': '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets'} # type: ignore + + + @distributed_trace_async + async def update_dataset( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + dataset_id: str, + force_update: Optional[bool] = False, + body: Optional["_models.Dataset"] = None, + **kwargs: Any + ) -> "_models.Dataset": + """Update a dataset. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param dataset_id: + :type dataset_id: str + :param force_update: + :type force_update: bool + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.Dataset + :keyword callable cls: A custom type or function that will be passed the direct response + :return: Dataset, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.Dataset + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.Dataset"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'Dataset') + else: + _json = None + + request = build_update_dataset_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_id=dataset_id, + content_type=content_type, + json=_json, + force_update=force_update, + template_url=self.update_dataset.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('Dataset', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + update_dataset.metadata = {'url': '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}'} # type: ignore + + + @distributed_trace_async + async def unregister_dataset( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + name: str, + **kwargs: Any + ) -> None: + """Unregister a dataset. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param name: + :type name: str + :keyword callable cls: A custom type or function that will be passed the direct response + :return: None, or the result of cls(response) + :rtype: None + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType[None] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + + request = build_unregister_dataset_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + name=name, + template_url=self.unregister_dataset.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in []: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + if cls: + return cls(pipeline_response, None, {}) + + unregister_dataset.metadata = {'url': '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{name}'} # type: ignore + diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_delete_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_delete_operations.py new file mode 100644 index 00000000..9bb293a5 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_delete_operations.py @@ -0,0 +1,104 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) AutoRest Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +import functools +from typing import Any, Callable, Dict, Generic, Optional, TypeVar +import warnings + +from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error +from azure.core.pipeline import PipelineResponse +from azure.core.pipeline.transport import AsyncHttpResponse +from azure.core.rest import HttpRequest +from azure.core.tracing.decorator_async import distributed_trace_async +from azure.mgmt.core.exceptions import ARMErrorFormat + +from ... import models as _models +from ..._vendor import _convert_request +from ...operations._delete_operations import build_data_container_request +T = TypeVar('T') +ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]] + +class DeleteOperations: + """DeleteOperations async operations. + + You should not instantiate this class directly. Instead, you should create a Client instance that + instantiates it for you and attaches it as an attribute. + + :ivar models: Alias to model classes used in this operation group. + :type models: ~azure.mgmt.machinelearningservices.models + :param client: Client for service requests. + :param config: Configuration of service client. + :param serializer: An object model serializer. + :param deserializer: An object model deserializer. + """ + + models = _models + + def __init__(self, client, config, serializer, deserializer) -> None: + self._client = client + self._serialize = serializer + self._deserialize = deserializer + self._config = config + + @distributed_trace_async + async def data_container( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + name: str, + **kwargs: Any + ) -> "_models.HttpResponseMessage": + """data_container. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param name: + :type name: str + :keyword callable cls: A custom type or function that will be passed the direct response + :return: HttpResponseMessage, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.HttpResponseMessage + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.HttpResponseMessage"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + + request = build_data_container_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + name=name, + template_url=self.data_container.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('HttpResponseMessage', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + data_container.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datacontainer/{name}'} # type: ignore + diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_get_operation_status_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_get_operation_status_operations.py new file mode 100644 index 00000000..273b307f --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_get_operation_status_operations.py @@ -0,0 +1,170 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) AutoRest Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +import functools +from typing import Any, Callable, Dict, Generic, Optional, TypeVar, Union +import warnings + +from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error +from azure.core.pipeline import PipelineResponse +from azure.core.pipeline.transport import AsyncHttpResponse +from azure.core.polling import AsyncLROPoller, AsyncNoPolling, AsyncPollingMethod +from azure.core.rest import HttpRequest +from azure.core.tracing.decorator_async import distributed_trace_async +from azure.mgmt.core.exceptions import ARMErrorFormat +from azure.mgmt.core.polling.async_arm_polling import AsyncARMPolling + +from ... import models as _models +from ..._vendor import _convert_request +from ...operations._get_operation_status_operations import build_get_dataset_operation_status_request_initial +T = TypeVar('T') +ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]] + +class GetOperationStatusOperations: + """GetOperationStatusOperations async operations. + + You should not instantiate this class directly. Instead, you should create a Client instance that + instantiates it for you and attaches it as an attribute. + + :ivar models: Alias to model classes used in this operation group. + :type models: ~azure.mgmt.machinelearningservices.models + :param client: Client for service requests. + :param config: Configuration of service client. + :param serializer: An object model serializer. + :param deserializer: An object model deserializer. + """ + + models = _models + + def __init__(self, client, config, serializer, deserializer) -> None: + self._client = client + self._serialize = serializer + self._deserialize = deserializer + self._config = config + + async def _get_dataset_operation_status_initial( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + operation_id: str, + **kwargs: Any + ) -> Optional["_models.LongRunningOperationResponse1LongRunningOperationResponseObject"]: + cls = kwargs.pop('cls', None) # type: ClsType[Optional["_models.LongRunningOperationResponse1LongRunningOperationResponseObject"]] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + + request = build_get_dataset_operation_status_request_initial( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + operation_id=operation_id, + template_url=self._get_dataset_operation_status_initial.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200, 202]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response, error_format=ARMErrorFormat) + + deserialized = None + response_headers = {} + if response.status_code == 200: + deserialized = self._deserialize('LongRunningOperationResponse1LongRunningOperationResponseObject', pipeline_response) + + if response.status_code == 202: + response_headers['Location']=self._deserialize('str', response.headers.get('Location')) + + + if cls: + return cls(pipeline_response, deserialized, response_headers) + + return deserialized + + _get_dataset_operation_status_initial.metadata = {'url': '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/operations/{operationId}'} # type: ignore + + + @distributed_trace_async + async def begin_get_dataset_operation_status( + self, + subscription_id: str, + resource_group_name: str, + workspace_name: str, + operation_id: str, + **kwargs: Any + ) -> AsyncLROPoller["_models.LongRunningOperationResponse1LongRunningOperationResponseObject"]: + """get_dataset_operation_status. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param operation_id: + :type operation_id: str + :keyword callable cls: A custom type or function that will be passed the direct response + :keyword str continuation_token: A continuation token to restart a poller from a saved state. + :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for + this operation to not poll, or pass in your own initialized polling object for a personal + polling strategy. + :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod + :keyword int polling_interval: Default waiting time between two polls for LRO operations if no + Retry-After header is present. + :return: An instance of AsyncLROPoller that returns either + LongRunningOperationResponse1LongRunningOperationResponseObject or the result of cls(response) + :rtype: + ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.LongRunningOperationResponse1LongRunningOperationResponseObject] + :raises: ~azure.core.exceptions.HttpResponseError + """ + polling = kwargs.pop('polling', True) # type: Union[bool, azure.core.polling.AsyncPollingMethod] + cls = kwargs.pop('cls', None) # type: ClsType["_models.LongRunningOperationResponse1LongRunningOperationResponseObject"] + lro_delay = kwargs.pop( + 'polling_interval', + self._config.polling_interval + ) + cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] + if cont_token is None: + raw_result = await self._get_dataset_operation_status_initial( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + operation_id=operation_id, + cls=lambda x,y,z: x, + **kwargs + ) + kwargs.pop('error_map', None) + + def get_long_running_output(pipeline_response): + response = pipeline_response.http_response + deserialized = self._deserialize('LongRunningOperationResponse1LongRunningOperationResponseObject', pipeline_response) + if cls: + return cls(pipeline_response, deserialized, {}) + return deserialized + + + if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, **kwargs) + elif polling is False: polling_method = AsyncNoPolling() + else: polling_method = polling + if cont_token: + return AsyncLROPoller.from_continuation_token( + polling_method=polling_method, + continuation_token=cont_token, + client=self._client, + deserialization_callback=get_long_running_output + ) + else: + return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) + + begin_get_dataset_operation_status.metadata = {'url': '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/operations/{operationId}'} # type: ignore diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/models/__init__.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/models/__init__.py new file mode 100644 index 00000000..42488e7c --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/models/__init__.py @@ -0,0 +1,187 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) AutoRest Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +try: + from ._models_py3 import ActionResult + from ._models_py3 import AssetId + from ._models_py3 import BatchDataUriResponse + from ._models_py3 import BatchGetResolvedURIs + from ._models_py3 import ColumnDefinition + from ._models_py3 import CreateUnregisteredInputData + from ._models_py3 import CreateUnregisteredOutputData + from ._models_py3 import DataCallRequest + from ._models_py3 import DataContainer + from ._models_py3 import DataContainerEntity + from ._models_py3 import DataContainerMutable + from ._models_py3 import DataField + from ._models_py3 import DataUriV2Response + from ._models_py3 import DataVersion + from ._models_py3 import DataVersionEntity + from ._models_py3 import DataVersionMutable + from ._models_py3 import DataViewSetResult + from ._models_py3 import Dataset + from ._models_py3 import DatasetDefinition + from ._models_py3 import DatasetDefinitionReference + from ._models_py3 import DatasetPath + from ._models_py3 import DatasetState + from ._models_py3 import DatasetV2 + from ._models_py3 import EntityMetadata + from ._models_py3 import ErrorAdditionalInfo + from ._models_py3 import ErrorResponse + from ._models_py3 import HistogramBin + from ._models_py3 import HttpContent + from ._models_py3 import HttpMethod + from ._models_py3 import HttpRequestMessage + from ._models_py3 import HttpResponseMessage + from ._models_py3 import InnerErrorResponse + from ._models_py3 import KeyValuePairStringIEnumerable1 + from ._models_py3 import LongRunningOperationResponse1LongRunningOperationResponseObject + from ._models_py3 import Moments + from ._models_py3 import PaginatedDataContainerEntityList + from ._models_py3 import PaginatedDataVersionEntityList + from ._models_py3 import PaginatedDatasetDefinitionList + from ._models_py3 import PaginatedDatasetList + from ._models_py3 import PaginatedDatasetV2List + from ._models_py3 import PaginatedStringList + from ._models_py3 import ProfileActionResult + from ._models_py3 import ProfileResult + from ._models_py3 import Quantiles + from ._models_py3 import RegisterExistingData + from ._models_py3 import RootError + from ._models_py3 import STypeCount + from ._models_py3 import SqlDataPath + from ._models_py3 import StoredProcedureParameter + from ._models_py3 import StringLengthCount + from ._models_py3 import TypeCount + from ._models_py3 import User + from ._models_py3 import ValueCount +except (SyntaxError, ImportError): + from ._models import ActionResult # type: ignore + from ._models import AssetId # type: ignore + from ._models import BatchDataUriResponse # type: ignore + from ._models import BatchGetResolvedURIs # type: ignore + from ._models import ColumnDefinition # type: ignore + from ._models import CreateUnregisteredInputData # type: ignore + from ._models import CreateUnregisteredOutputData # type: ignore + from ._models import DataCallRequest # type: ignore + from ._models import DataContainer # type: ignore + from ._models import DataContainerEntity # type: ignore + from ._models import DataContainerMutable # type: ignore + from ._models import DataField # type: ignore + from ._models import DataUriV2Response # type: ignore + from ._models import DataVersion # type: ignore + from ._models import DataVersionEntity # type: ignore + from ._models import DataVersionMutable # type: ignore + from ._models import DataViewSetResult # type: ignore + from ._models import Dataset # type: ignore + from ._models import DatasetDefinition # type: ignore + from ._models import DatasetDefinitionReference # type: ignore + from ._models import DatasetPath # type: ignore + from ._models import DatasetState # type: ignore + from ._models import DatasetV2 # type: ignore + from ._models import EntityMetadata # type: ignore + from ._models import ErrorAdditionalInfo # type: ignore + from ._models import ErrorResponse # type: ignore + from ._models import HistogramBin # type: ignore + from ._models import HttpContent # type: ignore + from ._models import HttpMethod # type: ignore + from ._models import HttpRequestMessage # type: ignore + from ._models import HttpResponseMessage # type: ignore + from ._models import InnerErrorResponse # type: ignore + from ._models import KeyValuePairStringIEnumerable1 # type: ignore + from ._models import LongRunningOperationResponse1LongRunningOperationResponseObject # type: ignore + from ._models import Moments # type: ignore + from ._models import PaginatedDataContainerEntityList # type: ignore + from ._models import PaginatedDataVersionEntityList # type: ignore + from ._models import PaginatedDatasetDefinitionList # type: ignore + from ._models import PaginatedDatasetList # type: ignore + from ._models import PaginatedDatasetV2List # type: ignore + from ._models import PaginatedStringList # type: ignore + from ._models import ProfileActionResult # type: ignore + from ._models import ProfileResult # type: ignore + from ._models import Quantiles # type: ignore + from ._models import RegisterExistingData # type: ignore + from ._models import RootError # type: ignore + from ._models import STypeCount # type: ignore + from ._models import SqlDataPath # type: ignore + from ._models import StoredProcedureParameter # type: ignore + from ._models import StringLengthCount # type: ignore + from ._models import TypeCount # type: ignore + from ._models import User # type: ignore + from ._models import ValueCount # type: ignore + +from ._azure_machine_learning_workspaces_enums import ( + DataflowType, + FieldType, + HttpStatusCode, + HttpVersionPolicy, + SType, + StoredProcedureParameterType, +) + +__all__ = [ + 'ActionResult', + 'AssetId', + 'BatchDataUriResponse', + 'BatchGetResolvedURIs', + 'ColumnDefinition', + 'CreateUnregisteredInputData', + 'CreateUnregisteredOutputData', + 'DataCallRequest', + 'DataContainer', + 'DataContainerEntity', + 'DataContainerMutable', + 'DataField', + 'DataUriV2Response', + 'DataVersion', + 'DataVersionEntity', + 'DataVersionMutable', + 'DataViewSetResult', + 'Dataset', + 'DatasetDefinition', + 'DatasetDefinitionReference', + 'DatasetPath', + 'DatasetState', + 'DatasetV2', + 'EntityMetadata', + 'ErrorAdditionalInfo', + 'ErrorResponse', + 'HistogramBin', + 'HttpContent', + 'HttpMethod', + 'HttpRequestMessage', + 'HttpResponseMessage', + 'InnerErrorResponse', + 'KeyValuePairStringIEnumerable1', + 'LongRunningOperationResponse1LongRunningOperationResponseObject', + 'Moments', + 'PaginatedDataContainerEntityList', + 'PaginatedDataVersionEntityList', + 'PaginatedDatasetDefinitionList', + 'PaginatedDatasetList', + 'PaginatedDatasetV2List', + 'PaginatedStringList', + 'ProfileActionResult', + 'ProfileResult', + 'Quantiles', + 'RegisterExistingData', + 'RootError', + 'STypeCount', + 'SqlDataPath', + 'StoredProcedureParameter', + 'StringLengthCount', + 'TypeCount', + 'User', + 'ValueCount', + 'DataflowType', + 'FieldType', + 'HttpStatusCode', + 'HttpVersionPolicy', + 'SType', + 'StoredProcedureParameterType', +] diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/models/_azure_machine_learning_workspaces_enums.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/models/_azure_machine_learning_workspaces_enums.py new file mode 100644 index 00000000..b06eab55 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/models/_azure_machine_learning_workspaces_enums.py @@ -0,0 +1,118 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) AutoRest Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +from enum import Enum +from azure.core import CaseInsensitiveEnumMeta + + +class DataflowType(str, Enum, metaclass=CaseInsensitiveEnumMeta): + + JSON = "Json" + YAML = "Yaml" + +class FieldType(str, Enum, metaclass=CaseInsensitiveEnumMeta): + + STRING = "String" + BOOLEAN = "Boolean" + INTEGER = "Integer" + DECIMAL = "Decimal" + DATE = "Date" + UNKNOWN = "Unknown" + ERROR = "Error" + NULL = "Null" + DATA_ROW = "DataRow" + LIST = "List" + STREAM = "Stream" + +class HttpStatusCode(str, Enum, metaclass=CaseInsensitiveEnumMeta): + + CONTINUE_ENUM = "Continue" + SWITCHING_PROTOCOLS = "SwitchingProtocols" + PROCESSING = "Processing" + EARLY_HINTS = "EarlyHints" + OK = "OK" + CREATED = "Created" + ACCEPTED = "Accepted" + NON_AUTHORITATIVE_INFORMATION = "NonAuthoritativeInformation" + NO_CONTENT = "NoContent" + RESET_CONTENT = "ResetContent" + PARTIAL_CONTENT = "PartialContent" + MULTI_STATUS = "MultiStatus" + ALREADY_REPORTED = "AlreadyReported" + IM_USED = "IMUsed" + AMBIGUOUS = "Ambiguous" + MOVED = "Moved" + REDIRECT = "Redirect" + REDIRECT_METHOD = "RedirectMethod" + NOT_MODIFIED = "NotModified" + USE_PROXY = "UseProxy" + UNUSED = "Unused" + TEMPORARY_REDIRECT = "TemporaryRedirect" + PERMANENT_REDIRECT = "PermanentRedirect" + BAD_REQUEST = "BadRequest" + UNAUTHORIZED = "Unauthorized" + PAYMENT_REQUIRED = "PaymentRequired" + FORBIDDEN = "Forbidden" + NOT_FOUND = "NotFound" + METHOD_NOT_ALLOWED = "MethodNotAllowed" + NOT_ACCEPTABLE = "NotAcceptable" + PROXY_AUTHENTICATION_REQUIRED = "ProxyAuthenticationRequired" + REQUEST_TIMEOUT = "RequestTimeout" + CONFLICT = "Conflict" + GONE = "Gone" + LENGTH_REQUIRED = "LengthRequired" + PRECONDITION_FAILED = "PreconditionFailed" + REQUEST_ENTITY_TOO_LARGE = "RequestEntityTooLarge" + REQUEST_URI_TOO_LONG = "RequestUriTooLong" + UNSUPPORTED_MEDIA_TYPE = "UnsupportedMediaType" + REQUESTED_RANGE_NOT_SATISFIABLE = "RequestedRangeNotSatisfiable" + EXPECTATION_FAILED = "ExpectationFailed" + MISDIRECTED_REQUEST = "MisdirectedRequest" + UNPROCESSABLE_ENTITY = "UnprocessableEntity" + LOCKED = "Locked" + FAILED_DEPENDENCY = "FailedDependency" + UPGRADE_REQUIRED = "UpgradeRequired" + PRECONDITION_REQUIRED = "PreconditionRequired" + TOO_MANY_REQUESTS = "TooManyRequests" + REQUEST_HEADER_FIELDS_TOO_LARGE = "RequestHeaderFieldsTooLarge" + UNAVAILABLE_FOR_LEGAL_REASONS = "UnavailableForLegalReasons" + INTERNAL_SERVER_ERROR = "InternalServerError" + NOT_IMPLEMENTED = "NotImplemented" + BAD_GATEWAY = "BadGateway" + SERVICE_UNAVAILABLE = "ServiceUnavailable" + GATEWAY_TIMEOUT = "GatewayTimeout" + HTTP_VERSION_NOT_SUPPORTED = "HttpVersionNotSupported" + VARIANT_ALSO_NEGOTIATES = "VariantAlsoNegotiates" + INSUFFICIENT_STORAGE = "InsufficientStorage" + LOOP_DETECTED = "LoopDetected" + NOT_EXTENDED = "NotExtended" + NETWORK_AUTHENTICATION_REQUIRED = "NetworkAuthenticationRequired" + +class HttpVersionPolicy(str, Enum, metaclass=CaseInsensitiveEnumMeta): + + REQUEST_VERSION_OR_LOWER = "RequestVersionOrLower" + REQUEST_VERSION_OR_HIGHER = "RequestVersionOrHigher" + REQUEST_VERSION_EXACT = "RequestVersionExact" + +class StoredProcedureParameterType(str, Enum, metaclass=CaseInsensitiveEnumMeta): + + STRING = "String" + INT = "Int" + DECIMAL = "Decimal" + GUID = "Guid" + BOOLEAN = "Boolean" + DATE = "Date" + +class SType(str, Enum, metaclass=CaseInsensitiveEnumMeta): + + EMAIL_ADDRESS = "EmailAddress" + GEOGRAPHIC_COORDINATE = "GeographicCoordinate" + IPV4_ADDRESS = "Ipv4Address" + IPV6_ADDRESS = "Ipv6Address" + US_PHONE_NUMBER = "UsPhoneNumber" + ZIP_CODE = "ZipCode" diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/models/_models.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/models/_models.py new file mode 100644 index 00000000..9bc1f683 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/models/_models.py @@ -0,0 +1,2608 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) AutoRest Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +from azure.core.exceptions import HttpResponseError +import msrest.serialization + + +class ActionResult(msrest.serialization.Model): + """ActionResult. + + :ivar is_up_to_date: + :vartype is_up_to_date: bool + :ivar is_up_to_date_error: + :vartype is_up_to_date_error: str + :ivar result_artifact_ids: + :vartype result_artifact_ids: list[str] + :ivar in_progress_action_id: + :vartype in_progress_action_id: str + :ivar run_id: + :vartype run_id: str + :ivar experiment_name: + :vartype experiment_name: str + :ivar datastore_name: + :vartype datastore_name: str + """ + + _attribute_map = { + 'is_up_to_date': {'key': 'isUpToDate', 'type': 'bool'}, + 'is_up_to_date_error': {'key': 'isUpToDateError', 'type': 'str'}, + 'result_artifact_ids': {'key': 'resultArtifactIds', 'type': '[str]'}, + 'in_progress_action_id': {'key': 'inProgressActionId', 'type': 'str'}, + 'run_id': {'key': 'runId', 'type': 'str'}, + 'experiment_name': {'key': 'experimentName', 'type': 'str'}, + 'datastore_name': {'key': 'datastoreName', 'type': 'str'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword is_up_to_date: + :paramtype is_up_to_date: bool + :keyword is_up_to_date_error: + :paramtype is_up_to_date_error: str + :keyword result_artifact_ids: + :paramtype result_artifact_ids: list[str] + :keyword in_progress_action_id: + :paramtype in_progress_action_id: str + :keyword run_id: + :paramtype run_id: str + :keyword experiment_name: + :paramtype experiment_name: str + :keyword datastore_name: + :paramtype datastore_name: str + """ + super(ActionResult, self).__init__(**kwargs) + self.is_up_to_date = kwargs.get('is_up_to_date', None) + self.is_up_to_date_error = kwargs.get('is_up_to_date_error', None) + self.result_artifact_ids = kwargs.get('result_artifact_ids', None) + self.in_progress_action_id = kwargs.get('in_progress_action_id', None) + self.run_id = kwargs.get('run_id', None) + self.experiment_name = kwargs.get('experiment_name', None) + self.datastore_name = kwargs.get('datastore_name', None) + + +class AssetId(msrest.serialization.Model): + """AssetId. + + All required parameters must be populated in order to send to Azure. + + :ivar value: Required. + :vartype value: str + """ + + _validation = { + 'value': {'required': True}, + } + + _attribute_map = { + 'value': {'key': 'value', 'type': 'str'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword value: Required. + :paramtype value: str + """ + super(AssetId, self).__init__(**kwargs) + self.value = kwargs['value'] + + +class BatchDataUriResponse(msrest.serialization.Model): + """BatchDataUriResponse. + + :ivar values: Dictionary of :code:`<DataUriV2Response>`. + :vartype values: dict[str, ~azure.mgmt.machinelearningservices.models.DataUriV2Response] + """ + + _attribute_map = { + 'values': {'key': 'values', 'type': '{DataUriV2Response}'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword values: Dictionary of :code:`<DataUriV2Response>`. + :paramtype values: dict[str, ~azure.mgmt.machinelearningservices.models.DataUriV2Response] + """ + super(BatchDataUriResponse, self).__init__(**kwargs) + self.values = kwargs.get('values', None) + + +class BatchGetResolvedURIs(msrest.serialization.Model): + """BatchGetResolvedURIs. + + All required parameters must be populated in order to send to Azure. + + :ivar values: Required. + :vartype values: list[str] + """ + + _validation = { + 'values': {'required': True}, + } + + _attribute_map = { + 'values': {'key': 'values', 'type': '[str]'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword values: Required. + :paramtype values: list[str] + """ + super(BatchGetResolvedURIs, self).__init__(**kwargs) + self.values = kwargs['values'] + + +class ColumnDefinition(msrest.serialization.Model): + """ColumnDefinition. + + :ivar id: + :vartype id: str + :ivar type: Possible values include: "String", "Boolean", "Integer", "Decimal", "Date", + "Unknown", "Error", "Null", "DataRow", "List", "Stream". + :vartype type: str or ~azure.mgmt.machinelearningservices.models.FieldType + """ + + _attribute_map = { + 'id': {'key': 'id', 'type': 'str'}, + 'type': {'key': 'type', 'type': 'str'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword id: + :paramtype id: str + :keyword type: Possible values include: "String", "Boolean", "Integer", "Decimal", "Date", + "Unknown", "Error", "Null", "DataRow", "List", "Stream". + :paramtype type: str or ~azure.mgmt.machinelearningservices.models.FieldType + """ + super(ColumnDefinition, self).__init__(**kwargs) + self.id = kwargs.get('id', None) + self.type = kwargs.get('type', None) + + +class CreateUnregisteredInputData(msrest.serialization.Model): + """CreateUnregisteredInputData. + + All required parameters must be populated in order to send to Azure. + + :ivar run_id: Required. + :vartype run_id: str + :ivar input_name: Required. + :vartype input_name: str + :ivar uri: Required. + :vartype uri: str + :ivar type: Required. + :vartype type: str + """ + + _validation = { + 'run_id': {'required': True}, + 'input_name': {'required': True}, + 'uri': {'required': True}, + 'type': {'required': True}, + } + + _attribute_map = { + 'run_id': {'key': 'runId', 'type': 'str'}, + 'input_name': {'key': 'inputName', 'type': 'str'}, + 'uri': {'key': 'uri', 'type': 'str'}, + 'type': {'key': 'type', 'type': 'str'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword run_id: Required. + :paramtype run_id: str + :keyword input_name: Required. + :paramtype input_name: str + :keyword uri: Required. + :paramtype uri: str + :keyword type: Required. + :paramtype type: str + """ + super(CreateUnregisteredInputData, self).__init__(**kwargs) + self.run_id = kwargs['run_id'] + self.input_name = kwargs['input_name'] + self.uri = kwargs['uri'] + self.type = kwargs['type'] + + +class CreateUnregisteredOutputData(msrest.serialization.Model): + """CreateUnregisteredOutputData. + + All required parameters must be populated in order to send to Azure. + + :ivar run_id: Required. + :vartype run_id: str + :ivar output_name: Required. + :vartype output_name: str + :ivar uri: Required. + :vartype uri: str + :ivar type: Required. + :vartype type: str + """ + + _validation = { + 'run_id': {'required': True}, + 'output_name': {'required': True}, + 'uri': {'required': True}, + 'type': {'required': True}, + } + + _attribute_map = { + 'run_id': {'key': 'runId', 'type': 'str'}, + 'output_name': {'key': 'outputName', 'type': 'str'}, + 'uri': {'key': 'uri', 'type': 'str'}, + 'type': {'key': 'type', 'type': 'str'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword run_id: Required. + :paramtype run_id: str + :keyword output_name: Required. + :paramtype output_name: str + :keyword uri: Required. + :paramtype uri: str + :keyword type: Required. + :paramtype type: str + """ + super(CreateUnregisteredOutputData, self).__init__(**kwargs) + self.run_id = kwargs['run_id'] + self.output_name = kwargs['output_name'] + self.uri = kwargs['uri'] + self.type = kwargs['type'] + + +class DataCallRequest(msrest.serialization.Model): + """DataCallRequest. + + :ivar data_uri: + :vartype data_uri: str + :ivar data_type: + :vartype data_type: str + :ivar asset_id: + :vartype asset_id: str + :ivar data_container_name: + :vartype data_container_name: str + :ivar version_id: + :vartype version_id: str + """ + + _attribute_map = { + 'data_uri': {'key': 'dataUri', 'type': 'str'}, + 'data_type': {'key': 'dataType', 'type': 'str'}, + 'asset_id': {'key': 'assetId', 'type': 'str'}, + 'data_container_name': {'key': 'dataContainerName', 'type': 'str'}, + 'version_id': {'key': 'versionId', 'type': 'str'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword data_uri: + :paramtype data_uri: str + :keyword data_type: + :paramtype data_type: str + :keyword asset_id: + :paramtype asset_id: str + :keyword data_container_name: + :paramtype data_container_name: str + :keyword version_id: + :paramtype version_id: str + """ + super(DataCallRequest, self).__init__(**kwargs) + self.data_uri = kwargs.get('data_uri', None) + self.data_type = kwargs.get('data_type', None) + self.asset_id = kwargs.get('asset_id', None) + self.data_container_name = kwargs.get('data_container_name', None) + self.version_id = kwargs.get('version_id', None) + + +class DataContainer(msrest.serialization.Model): + """DataContainer. + + All required parameters must be populated in order to send to Azure. + + :ivar name: Required. + :vartype name: str + :ivar data_type: Required. + :vartype data_type: str + :ivar mutable_props: + :vartype mutable_props: ~azure.mgmt.machinelearningservices.models.DataContainerMutable + :ivar is_registered: + :vartype is_registered: bool + """ + + _validation = { + 'name': {'required': True}, + 'data_type': {'required': True}, + } + + _attribute_map = { + 'name': {'key': 'name', 'type': 'str'}, + 'data_type': {'key': 'dataType', 'type': 'str'}, + 'mutable_props': {'key': 'mutableProps', 'type': 'DataContainerMutable'}, + 'is_registered': {'key': 'isRegistered', 'type': 'bool'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword name: Required. + :paramtype name: str + :keyword data_type: Required. + :paramtype data_type: str + :keyword mutable_props: + :paramtype mutable_props: ~azure.mgmt.machinelearningservices.models.DataContainerMutable + :keyword is_registered: + :paramtype is_registered: bool + """ + super(DataContainer, self).__init__(**kwargs) + self.name = kwargs['name'] + self.data_type = kwargs['data_type'] + self.mutable_props = kwargs.get('mutable_props', None) + self.is_registered = kwargs.get('is_registered', None) + + +class DataContainerEntity(msrest.serialization.Model): + """DataContainerEntity. + + :ivar data_container: + :vartype data_container: ~azure.mgmt.machinelearningservices.models.DataContainer + :ivar entity_metadata: + :vartype entity_metadata: ~azure.mgmt.machinelearningservices.models.EntityMetadata + :ivar latest_version: + :vartype latest_version: ~azure.mgmt.machinelearningservices.models.DataVersionEntity + :ivar next_version_id: + :vartype next_version_id: str + :ivar legacy_dataset_type: + :vartype legacy_dataset_type: str + """ + + _attribute_map = { + 'data_container': {'key': 'dataContainer', 'type': 'DataContainer'}, + 'entity_metadata': {'key': 'entityMetadata', 'type': 'EntityMetadata'}, + 'latest_version': {'key': 'latestVersion', 'type': 'DataVersionEntity'}, + 'next_version_id': {'key': 'nextVersionId', 'type': 'str'}, + 'legacy_dataset_type': {'key': 'legacyDatasetType', 'type': 'str'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword data_container: + :paramtype data_container: ~azure.mgmt.machinelearningservices.models.DataContainer + :keyword entity_metadata: + :paramtype entity_metadata: ~azure.mgmt.machinelearningservices.models.EntityMetadata + :keyword latest_version: + :paramtype latest_version: ~azure.mgmt.machinelearningservices.models.DataVersionEntity + :keyword next_version_id: + :paramtype next_version_id: str + :keyword legacy_dataset_type: + :paramtype legacy_dataset_type: str + """ + super(DataContainerEntity, self).__init__(**kwargs) + self.data_container = kwargs.get('data_container', None) + self.entity_metadata = kwargs.get('entity_metadata', None) + self.latest_version = kwargs.get('latest_version', None) + self.next_version_id = kwargs.get('next_version_id', None) + self.legacy_dataset_type = kwargs.get('legacy_dataset_type', None) + + +class DataContainerMutable(msrest.serialization.Model): + """DataContainerMutable. + + :ivar description: + :vartype description: str + :ivar tags: A set of tags. Dictionary of :code:`<string>`. + :vartype tags: dict[str, str] + :ivar is_archived: + :vartype is_archived: bool + """ + + _attribute_map = { + 'description': {'key': 'description', 'type': 'str'}, + 'tags': {'key': 'tags', 'type': '{str}'}, + 'is_archived': {'key': 'isArchived', 'type': 'bool'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword description: + :paramtype description: str + :keyword tags: A set of tags. Dictionary of :code:`<string>`. + :paramtype tags: dict[str, str] + :keyword is_archived: + :paramtype is_archived: bool + """ + super(DataContainerMutable, self).__init__(**kwargs) + self.description = kwargs.get('description', None) + self.tags = kwargs.get('tags', None) + self.is_archived = kwargs.get('is_archived', None) + + +class DataField(msrest.serialization.Model): + """DataField. + + :ivar type: Possible values include: "String", "Boolean", "Integer", "Decimal", "Date", + "Unknown", "Error", "Null", "DataRow", "List", "Stream". + :vartype type: str or ~azure.mgmt.machinelearningservices.models.FieldType + :ivar value: Anything. + :vartype value: any + """ + + _attribute_map = { + 'type': {'key': 'type', 'type': 'str'}, + 'value': {'key': 'value', 'type': 'object'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword type: Possible values include: "String", "Boolean", "Integer", "Decimal", "Date", + "Unknown", "Error", "Null", "DataRow", "List", "Stream". + :paramtype type: str or ~azure.mgmt.machinelearningservices.models.FieldType + :keyword value: Anything. + :paramtype value: any + """ + super(DataField, self).__init__(**kwargs) + self.type = kwargs.get('type', None) + self.value = kwargs.get('value', None) + + +class Dataset(msrest.serialization.Model): + """Dataset. + + :ivar dataset_id: + :vartype dataset_id: str + :ivar dataset_state: + :vartype dataset_state: ~azure.mgmt.machinelearningservices.models.DatasetState + :ivar latest: + :vartype latest: ~azure.mgmt.machinelearningservices.models.DatasetDefinition + :ivar next_version_id: + :vartype next_version_id: str + :ivar created_time: + :vartype created_time: ~datetime.datetime + :ivar modified_time: + :vartype modified_time: ~datetime.datetime + :ivar etag: + :vartype etag: str + :ivar name: + :vartype name: str + :ivar description: + :vartype description: str + :ivar tags: A set of tags. Dictionary of :code:`<string>`. + :vartype tags: dict[str, str] + :ivar is_visible: + :vartype is_visible: bool + :ivar default_compute: + :vartype default_compute: str + :ivar dataset_type: + :vartype dataset_type: str + """ + + _attribute_map = { + 'dataset_id': {'key': 'datasetId', 'type': 'str'}, + 'dataset_state': {'key': 'datasetState', 'type': 'DatasetState'}, + 'latest': {'key': 'latest', 'type': 'DatasetDefinition'}, + 'next_version_id': {'key': 'nextVersionId', 'type': 'str'}, + 'created_time': {'key': 'createdTime', 'type': 'iso-8601'}, + 'modified_time': {'key': 'modifiedTime', 'type': 'iso-8601'}, + 'etag': {'key': 'etag', 'type': 'str'}, + 'name': {'key': 'name', 'type': 'str'}, + 'description': {'key': 'description', 'type': 'str'}, + 'tags': {'key': 'tags', 'type': '{str}'}, + 'is_visible': {'key': 'isVisible', 'type': 'bool'}, + 'default_compute': {'key': 'defaultCompute', 'type': 'str'}, + 'dataset_type': {'key': 'datasetType', 'type': 'str'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword dataset_id: + :paramtype dataset_id: str + :keyword dataset_state: + :paramtype dataset_state: ~azure.mgmt.machinelearningservices.models.DatasetState + :keyword latest: + :paramtype latest: ~azure.mgmt.machinelearningservices.models.DatasetDefinition + :keyword next_version_id: + :paramtype next_version_id: str + :keyword created_time: + :paramtype created_time: ~datetime.datetime + :keyword modified_time: + :paramtype modified_time: ~datetime.datetime + :keyword etag: + :paramtype etag: str + :keyword name: + :paramtype name: str + :keyword description: + :paramtype description: str + :keyword tags: A set of tags. Dictionary of :code:`<string>`. + :paramtype tags: dict[str, str] + :keyword is_visible: + :paramtype is_visible: bool + :keyword default_compute: + :paramtype default_compute: str + :keyword dataset_type: + :paramtype dataset_type: str + """ + super(Dataset, self).__init__(**kwargs) + self.dataset_id = kwargs.get('dataset_id', None) + self.dataset_state = kwargs.get('dataset_state', None) + self.latest = kwargs.get('latest', None) + self.next_version_id = kwargs.get('next_version_id', None) + self.created_time = kwargs.get('created_time', None) + self.modified_time = kwargs.get('modified_time', None) + self.etag = kwargs.get('etag', None) + self.name = kwargs.get('name', None) + self.description = kwargs.get('description', None) + self.tags = kwargs.get('tags', None) + self.is_visible = kwargs.get('is_visible', None) + self.default_compute = kwargs.get('default_compute', None) + self.dataset_type = kwargs.get('dataset_type', None) + + +class DatasetDefinition(msrest.serialization.Model): + """DatasetDefinition. + + :ivar dataset_id: + :vartype dataset_id: str + :ivar version_id: + :vartype version_id: str + :ivar dataset_definition_state: + :vartype dataset_definition_state: ~azure.mgmt.machinelearningservices.models.DatasetState + :ivar dataflow: + :vartype dataflow: str + :ivar dataflow_type: Possible values include: "Json", "Yaml". + :vartype dataflow_type: str or ~azure.mgmt.machinelearningservices.models.DataflowType + :ivar data_path: + :vartype data_path: ~azure.mgmt.machinelearningservices.models.DatasetPath + :ivar partition_format_in_path: + :vartype partition_format_in_path: str + :ivar profile_action_result: + :vartype profile_action_result: ~azure.mgmt.machinelearningservices.models.ProfileActionResult + :ivar notes: + :vartype notes: str + :ivar etag: + :vartype etag: str + :ivar created_time: + :vartype created_time: ~datetime.datetime + :ivar modified_time: + :vartype modified_time: ~datetime.datetime + :ivar data_expiry_time: + :vartype data_expiry_time: ~datetime.datetime + :ivar created_by: + :vartype created_by: ~azure.mgmt.machinelearningservices.models.User + :ivar modified_by: + :vartype modified_by: ~azure.mgmt.machinelearningservices.models.User + :ivar file_type: + :vartype file_type: str + :ivar properties: Dictionary of :code:`<any>`. + :vartype properties: dict[str, any] + :ivar saved_dataset_id: + :vartype saved_dataset_id: str + :ivar telemetry_info: Dictionary of :code:`<string>`. + :vartype telemetry_info: dict[str, str] + :ivar use_description_tags_from_definition: + :vartype use_description_tags_from_definition: bool + :ivar description: + :vartype description: str + :ivar tags: A set of tags. Dictionary of :code:`<string>`. + :vartype tags: dict[str, str] + """ + + _attribute_map = { + 'dataset_id': {'key': 'datasetId', 'type': 'str'}, + 'version_id': {'key': 'versionId', 'type': 'str'}, + 'dataset_definition_state': {'key': 'datasetDefinitionState', 'type': 'DatasetState'}, + 'dataflow': {'key': 'dataflow', 'type': 'str'}, + 'dataflow_type': {'key': 'dataflowType', 'type': 'str'}, + 'data_path': {'key': 'dataPath', 'type': 'DatasetPath'}, + 'partition_format_in_path': {'key': 'partitionFormatInPath', 'type': 'str'}, + 'profile_action_result': {'key': 'profileActionResult', 'type': 'ProfileActionResult'}, + 'notes': {'key': 'notes', 'type': 'str'}, + 'etag': {'key': 'etag', 'type': 'str'}, + 'created_time': {'key': 'createdTime', 'type': 'iso-8601'}, + 'modified_time': {'key': 'modifiedTime', 'type': 'iso-8601'}, + 'data_expiry_time': {'key': 'dataExpiryTime', 'type': 'iso-8601'}, + 'created_by': {'key': 'createdBy', 'type': 'User'}, + 'modified_by': {'key': 'modifiedBy', 'type': 'User'}, + 'file_type': {'key': 'fileType', 'type': 'str'}, + 'properties': {'key': 'properties', 'type': '{object}'}, + 'saved_dataset_id': {'key': 'savedDatasetId', 'type': 'str'}, + 'telemetry_info': {'key': 'telemetryInfo', 'type': '{str}'}, + 'use_description_tags_from_definition': {'key': 'useDescriptionTagsFromDefinition', 'type': 'bool'}, + 'description': {'key': 'description', 'type': 'str'}, + 'tags': {'key': 'tags', 'type': '{str}'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword dataset_id: + :paramtype dataset_id: str + :keyword version_id: + :paramtype version_id: str + :keyword dataset_definition_state: + :paramtype dataset_definition_state: ~azure.mgmt.machinelearningservices.models.DatasetState + :keyword dataflow: + :paramtype dataflow: str + :keyword dataflow_type: Possible values include: "Json", "Yaml". + :paramtype dataflow_type: str or ~azure.mgmt.machinelearningservices.models.DataflowType + :keyword data_path: + :paramtype data_path: ~azure.mgmt.machinelearningservices.models.DatasetPath + :keyword partition_format_in_path: + :paramtype partition_format_in_path: str + :keyword profile_action_result: + :paramtype profile_action_result: + ~azure.mgmt.machinelearningservices.models.ProfileActionResult + :keyword notes: + :paramtype notes: str + :keyword etag: + :paramtype etag: str + :keyword created_time: + :paramtype created_time: ~datetime.datetime + :keyword modified_time: + :paramtype modified_time: ~datetime.datetime + :keyword data_expiry_time: + :paramtype data_expiry_time: ~datetime.datetime + :keyword created_by: + :paramtype created_by: ~azure.mgmt.machinelearningservices.models.User + :keyword modified_by: + :paramtype modified_by: ~azure.mgmt.machinelearningservices.models.User + :keyword file_type: + :paramtype file_type: str + :keyword properties: Dictionary of :code:`<any>`. + :paramtype properties: dict[str, any] + :keyword saved_dataset_id: + :paramtype saved_dataset_id: str + :keyword telemetry_info: Dictionary of :code:`<string>`. + :paramtype telemetry_info: dict[str, str] + :keyword use_description_tags_from_definition: + :paramtype use_description_tags_from_definition: bool + :keyword description: + :paramtype description: str + :keyword tags: A set of tags. Dictionary of :code:`<string>`. + :paramtype tags: dict[str, str] + """ + super(DatasetDefinition, self).__init__(**kwargs) + self.dataset_id = kwargs.get('dataset_id', None) + self.version_id = kwargs.get('version_id', None) + self.dataset_definition_state = kwargs.get('dataset_definition_state', None) + self.dataflow = kwargs.get('dataflow', None) + self.dataflow_type = kwargs.get('dataflow_type', None) + self.data_path = kwargs.get('data_path', None) + self.partition_format_in_path = kwargs.get('partition_format_in_path', None) + self.profile_action_result = kwargs.get('profile_action_result', None) + self.notes = kwargs.get('notes', None) + self.etag = kwargs.get('etag', None) + self.created_time = kwargs.get('created_time', None) + self.modified_time = kwargs.get('modified_time', None) + self.data_expiry_time = kwargs.get('data_expiry_time', None) + self.created_by = kwargs.get('created_by', None) + self.modified_by = kwargs.get('modified_by', None) + self.file_type = kwargs.get('file_type', None) + self.properties = kwargs.get('properties', None) + self.saved_dataset_id = kwargs.get('saved_dataset_id', None) + self.telemetry_info = kwargs.get('telemetry_info', None) + self.use_description_tags_from_definition = kwargs.get('use_description_tags_from_definition', None) + self.description = kwargs.get('description', None) + self.tags = kwargs.get('tags', None) + + +class DatasetDefinitionReference(msrest.serialization.Model): + """DatasetDefinitionReference. + + :ivar dataset_id: + :vartype dataset_id: str + :ivar definition_version: + :vartype definition_version: str + """ + + _attribute_map = { + 'dataset_id': {'key': 'datasetId', 'type': 'str'}, + 'definition_version': {'key': 'definitionVersion', 'type': 'str'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword dataset_id: + :paramtype dataset_id: str + :keyword definition_version: + :paramtype definition_version: str + """ + super(DatasetDefinitionReference, self).__init__(**kwargs) + self.dataset_id = kwargs.get('dataset_id', None) + self.definition_version = kwargs.get('definition_version', None) + + +class DatasetPath(msrest.serialization.Model): + """DatasetPath. + + :ivar datastore_name: + :vartype datastore_name: str + :ivar relative_path: + :vartype relative_path: str + :ivar azure_file_path: + :vartype azure_file_path: str + :ivar paths: + :vartype paths: list[str] + :ivar sql_data_path: + :vartype sql_data_path: ~azure.mgmt.machinelearningservices.models.SqlDataPath + :ivar http_url: + :vartype http_url: str + :ivar additional_properties: Dictionary of :code:`<any>`. + :vartype additional_properties: dict[str, any] + :ivar partition_format: + :vartype partition_format: str + :ivar partition_format_ignore_error: + :vartype partition_format_ignore_error: bool + """ + + _attribute_map = { + 'datastore_name': {'key': 'datastoreName', 'type': 'str'}, + 'relative_path': {'key': 'relativePath', 'type': 'str'}, + 'azure_file_path': {'key': 'azureFilePath', 'type': 'str'}, + 'paths': {'key': 'paths', 'type': '[str]'}, + 'sql_data_path': {'key': 'sqlDataPath', 'type': 'SqlDataPath'}, + 'http_url': {'key': 'httpUrl', 'type': 'str'}, + 'additional_properties': {'key': 'additionalProperties', 'type': '{object}'}, + 'partition_format': {'key': 'partitionFormat', 'type': 'str'}, + 'partition_format_ignore_error': {'key': 'partitionFormatIgnoreError', 'type': 'bool'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword datastore_name: + :paramtype datastore_name: str + :keyword relative_path: + :paramtype relative_path: str + :keyword azure_file_path: + :paramtype azure_file_path: str + :keyword paths: + :paramtype paths: list[str] + :keyword sql_data_path: + :paramtype sql_data_path: ~azure.mgmt.machinelearningservices.models.SqlDataPath + :keyword http_url: + :paramtype http_url: str + :keyword additional_properties: Dictionary of :code:`<any>`. + :paramtype additional_properties: dict[str, any] + :keyword partition_format: + :paramtype partition_format: str + :keyword partition_format_ignore_error: + :paramtype partition_format_ignore_error: bool + """ + super(DatasetPath, self).__init__(**kwargs) + self.datastore_name = kwargs.get('datastore_name', None) + self.relative_path = kwargs.get('relative_path', None) + self.azure_file_path = kwargs.get('azure_file_path', None) + self.paths = kwargs.get('paths', None) + self.sql_data_path = kwargs.get('sql_data_path', None) + self.http_url = kwargs.get('http_url', None) + self.additional_properties = kwargs.get('additional_properties', None) + self.partition_format = kwargs.get('partition_format', None) + self.partition_format_ignore_error = kwargs.get('partition_format_ignore_error', None) + + +class DatasetState(msrest.serialization.Model): + """DatasetState. + + :ivar state: + :vartype state: str + :ivar deprecated_by: + :vartype deprecated_by: ~azure.mgmt.machinelearningservices.models.DatasetDefinitionReference + :ivar etag: + :vartype etag: str + """ + + _attribute_map = { + 'state': {'key': 'state', 'type': 'str'}, + 'deprecated_by': {'key': 'deprecatedBy', 'type': 'DatasetDefinitionReference'}, + 'etag': {'key': 'etag', 'type': 'str'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword state: + :paramtype state: str + :keyword deprecated_by: + :paramtype deprecated_by: ~azure.mgmt.machinelearningservices.models.DatasetDefinitionReference + :keyword etag: + :paramtype etag: str + """ + super(DatasetState, self).__init__(**kwargs) + self.state = kwargs.get('state', None) + self.deprecated_by = kwargs.get('deprecated_by', None) + self.etag = kwargs.get('etag', None) + + +class DatasetV2(msrest.serialization.Model): + """DatasetV2. + + :ivar dataset_id: + :vartype dataset_id: str + :ivar name: + :vartype name: str + :ivar version_id: + :vartype version_id: str + :ivar dataflow: + :vartype dataflow: str + :ivar created_time: + :vartype created_time: ~datetime.datetime + :ivar modified_time: + :vartype modified_time: ~datetime.datetime + :ivar created_by: + :vartype created_by: ~azure.mgmt.machinelearningservices.models.User + :ivar modified_by: + :vartype modified_by: ~azure.mgmt.machinelearningservices.models.User + :ivar properties: Dictionary of :code:`<string>`. + :vartype properties: dict[str, str] + :ivar telemetry_info: Dictionary of :code:`<string>`. + :vartype telemetry_info: dict[str, str] + :ivar description: + :vartype description: str + :ivar is_anonymous: + :vartype is_anonymous: bool + :ivar tags: A set of tags. Dictionary of :code:`<string>`. + :vartype tags: dict[str, str] + :ivar legacy_properties: Dictionary of :code:`<any>`. + :vartype legacy_properties: dict[str, any] + :ivar data_expiry_time: + :vartype data_expiry_time: ~datetime.datetime + :ivar legacy: Dictionary of :code:`<any>`. + :vartype legacy: dict[str, any] + """ + + _attribute_map = { + 'dataset_id': {'key': 'datasetId', 'type': 'str'}, + 'name': {'key': 'name', 'type': 'str'}, + 'version_id': {'key': 'versionId', 'type': 'str'}, + 'dataflow': {'key': 'dataflow', 'type': 'str'}, + 'created_time': {'key': 'createdTime', 'type': 'iso-8601'}, + 'modified_time': {'key': 'modifiedTime', 'type': 'iso-8601'}, + 'created_by': {'key': 'createdBy', 'type': 'User'}, + 'modified_by': {'key': 'modifiedBy', 'type': 'User'}, + 'properties': {'key': 'properties', 'type': '{str}'}, + 'telemetry_info': {'key': 'telemetryInfo', 'type': '{str}'}, + 'description': {'key': 'description', 'type': 'str'}, + 'is_anonymous': {'key': 'isAnonymous', 'type': 'bool'}, + 'tags': {'key': 'tags', 'type': '{str}'}, + 'legacy_properties': {'key': 'legacyProperties', 'type': '{object}'}, + 'data_expiry_time': {'key': 'dataExpiryTime', 'type': 'iso-8601'}, + 'legacy': {'key': 'legacy', 'type': '{object}'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword dataset_id: + :paramtype dataset_id: str + :keyword name: + :paramtype name: str + :keyword version_id: + :paramtype version_id: str + :keyword dataflow: + :paramtype dataflow: str + :keyword created_time: + :paramtype created_time: ~datetime.datetime + :keyword modified_time: + :paramtype modified_time: ~datetime.datetime + :keyword created_by: + :paramtype created_by: ~azure.mgmt.machinelearningservices.models.User + :keyword modified_by: + :paramtype modified_by: ~azure.mgmt.machinelearningservices.models.User + :keyword properties: Dictionary of :code:`<string>`. + :paramtype properties: dict[str, str] + :keyword telemetry_info: Dictionary of :code:`<string>`. + :paramtype telemetry_info: dict[str, str] + :keyword description: + :paramtype description: str + :keyword is_anonymous: + :paramtype is_anonymous: bool + :keyword tags: A set of tags. Dictionary of :code:`<string>`. + :paramtype tags: dict[str, str] + :keyword legacy_properties: Dictionary of :code:`<any>`. + :paramtype legacy_properties: dict[str, any] + :keyword data_expiry_time: + :paramtype data_expiry_time: ~datetime.datetime + :keyword legacy: Dictionary of :code:`<any>`. + :paramtype legacy: dict[str, any] + """ + super(DatasetV2, self).__init__(**kwargs) + self.dataset_id = kwargs.get('dataset_id', None) + self.name = kwargs.get('name', None) + self.version_id = kwargs.get('version_id', None) + self.dataflow = kwargs.get('dataflow', None) + self.created_time = kwargs.get('created_time', None) + self.modified_time = kwargs.get('modified_time', None) + self.created_by = kwargs.get('created_by', None) + self.modified_by = kwargs.get('modified_by', None) + self.properties = kwargs.get('properties', None) + self.telemetry_info = kwargs.get('telemetry_info', None) + self.description = kwargs.get('description', None) + self.is_anonymous = kwargs.get('is_anonymous', None) + self.tags = kwargs.get('tags', None) + self.legacy_properties = kwargs.get('legacy_properties', None) + self.data_expiry_time = kwargs.get('data_expiry_time', None) + self.legacy = kwargs.get('legacy', None) + + +class DataUriV2Response(msrest.serialization.Model): + """DataUriV2Response. + + :ivar uri: + :vartype uri: str + :ivar type: + :vartype type: str + """ + + _attribute_map = { + 'uri': {'key': 'uri', 'type': 'str'}, + 'type': {'key': 'type', 'type': 'str'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword uri: + :paramtype uri: str + :keyword type: + :paramtype type: str + """ + super(DataUriV2Response, self).__init__(**kwargs) + self.uri = kwargs.get('uri', None) + self.type = kwargs.get('type', None) + + +class DataVersion(msrest.serialization.Model): + """DataVersion. + + All required parameters must be populated in order to send to Azure. + + :ivar asset_id: + :vartype asset_id: str + :ivar data_container_name: Required. + :vartype data_container_name: str + :ivar data_type: Required. + :vartype data_type: str + :ivar data_uri: Required. + :vartype data_uri: str + :ivar version_id: Required. + :vartype version_id: str + :ivar mutable_props: + :vartype mutable_props: ~azure.mgmt.machinelearningservices.models.DataVersionMutable + :ivar referenced_data_uris: + :vartype referenced_data_uris: list[str] + :ivar properties: Dictionary of :code:`<string>`. + :vartype properties: dict[str, str] + """ + + _validation = { + 'data_container_name': {'required': True}, + 'data_type': {'required': True}, + 'data_uri': {'required': True}, + 'version_id': {'required': True}, + } + + _attribute_map = { + 'asset_id': {'key': 'assetId', 'type': 'str'}, + 'data_container_name': {'key': 'dataContainerName', 'type': 'str'}, + 'data_type': {'key': 'dataType', 'type': 'str'}, + 'data_uri': {'key': 'dataUri', 'type': 'str'}, + 'version_id': {'key': 'versionId', 'type': 'str'}, + 'mutable_props': {'key': 'mutableProps', 'type': 'DataVersionMutable'}, + 'referenced_data_uris': {'key': 'referencedDataUris', 'type': '[str]'}, + 'properties': {'key': 'properties', 'type': '{str}'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword asset_id: + :paramtype asset_id: str + :keyword data_container_name: Required. + :paramtype data_container_name: str + :keyword data_type: Required. + :paramtype data_type: str + :keyword data_uri: Required. + :paramtype data_uri: str + :keyword version_id: Required. + :paramtype version_id: str + :keyword mutable_props: + :paramtype mutable_props: ~azure.mgmt.machinelearningservices.models.DataVersionMutable + :keyword referenced_data_uris: + :paramtype referenced_data_uris: list[str] + :keyword properties: Dictionary of :code:`<string>`. + :paramtype properties: dict[str, str] + """ + super(DataVersion, self).__init__(**kwargs) + self.asset_id = kwargs.get('asset_id', None) + self.data_container_name = kwargs['data_container_name'] + self.data_type = kwargs['data_type'] + self.data_uri = kwargs['data_uri'] + self.version_id = kwargs['version_id'] + self.mutable_props = kwargs.get('mutable_props', None) + self.referenced_data_uris = kwargs.get('referenced_data_uris', None) + self.properties = kwargs.get('properties', None) + + +class DataVersionEntity(msrest.serialization.Model): + """DataVersionEntity. + + :ivar data_version: + :vartype data_version: ~azure.mgmt.machinelearningservices.models.DataVersion + :ivar entity_metadata: + :vartype entity_metadata: ~azure.mgmt.machinelearningservices.models.EntityMetadata + """ + + _attribute_map = { + 'data_version': {'key': 'dataVersion', 'type': 'DataVersion'}, + 'entity_metadata': {'key': 'entityMetadata', 'type': 'EntityMetadata'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword data_version: + :paramtype data_version: ~azure.mgmt.machinelearningservices.models.DataVersion + :keyword entity_metadata: + :paramtype entity_metadata: ~azure.mgmt.machinelearningservices.models.EntityMetadata + """ + super(DataVersionEntity, self).__init__(**kwargs) + self.data_version = kwargs.get('data_version', None) + self.entity_metadata = kwargs.get('entity_metadata', None) + + +class DataVersionMutable(msrest.serialization.Model): + """DataVersionMutable. + + :ivar data_expiry_time: + :vartype data_expiry_time: ~datetime.datetime + :ivar description: + :vartype description: str + :ivar tags: A set of tags. Dictionary of :code:`<string>`. + :vartype tags: dict[str, str] + :ivar is_archived: + :vartype is_archived: bool + """ + + _attribute_map = { + 'data_expiry_time': {'key': 'dataExpiryTime', 'type': 'iso-8601'}, + 'description': {'key': 'description', 'type': 'str'}, + 'tags': {'key': 'tags', 'type': '{str}'}, + 'is_archived': {'key': 'isArchived', 'type': 'bool'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword data_expiry_time: + :paramtype data_expiry_time: ~datetime.datetime + :keyword description: + :paramtype description: str + :keyword tags: A set of tags. Dictionary of :code:`<string>`. + :paramtype tags: dict[str, str] + :keyword is_archived: + :paramtype is_archived: bool + """ + super(DataVersionMutable, self).__init__(**kwargs) + self.data_expiry_time = kwargs.get('data_expiry_time', None) + self.description = kwargs.get('description', None) + self.tags = kwargs.get('tags', None) + self.is_archived = kwargs.get('is_archived', None) + + +class DataViewSetResult(msrest.serialization.Model): + """DataViewSetResult. + + :ivar schema: + :vartype schema: list[~azure.mgmt.machinelearningservices.models.ColumnDefinition] + :ivar rows: + :vartype rows: list[list[~azure.mgmt.machinelearningservices.models.DataField]] + """ + + _attribute_map = { + 'schema': {'key': 'schema', 'type': '[ColumnDefinition]'}, + 'rows': {'key': 'rows', 'type': '[[DataField]]'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword schema: + :paramtype schema: list[~azure.mgmt.machinelearningservices.models.ColumnDefinition] + :keyword rows: + :paramtype rows: list[list[~azure.mgmt.machinelearningservices.models.DataField]] + """ + super(DataViewSetResult, self).__init__(**kwargs) + self.schema = kwargs.get('schema', None) + self.rows = kwargs.get('rows', None) + + +class EntityMetadata(msrest.serialization.Model): + """EntityMetadata. + + :ivar etag: + :vartype etag: str + :ivar created_time: + :vartype created_time: ~datetime.datetime + :ivar modified_time: + :vartype modified_time: ~datetime.datetime + :ivar created_by: + :vartype created_by: ~azure.mgmt.machinelearningservices.models.User + :ivar modified_by: + :vartype modified_by: ~azure.mgmt.machinelearningservices.models.User + """ + + _attribute_map = { + 'etag': {'key': 'etag', 'type': 'str'}, + 'created_time': {'key': 'createdTime', 'type': 'iso-8601'}, + 'modified_time': {'key': 'modifiedTime', 'type': 'iso-8601'}, + 'created_by': {'key': 'createdBy', 'type': 'User'}, + 'modified_by': {'key': 'modifiedBy', 'type': 'User'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword etag: + :paramtype etag: str + :keyword created_time: + :paramtype created_time: ~datetime.datetime + :keyword modified_time: + :paramtype modified_time: ~datetime.datetime + :keyword created_by: + :paramtype created_by: ~azure.mgmt.machinelearningservices.models.User + :keyword modified_by: + :paramtype modified_by: ~azure.mgmt.machinelearningservices.models.User + """ + super(EntityMetadata, self).__init__(**kwargs) + self.etag = kwargs.get('etag', None) + self.created_time = kwargs.get('created_time', None) + self.modified_time = kwargs.get('modified_time', None) + self.created_by = kwargs.get('created_by', None) + self.modified_by = kwargs.get('modified_by', None) + + +class ErrorAdditionalInfo(msrest.serialization.Model): + """The resource management error additional info. + + :ivar type: The additional info type. + :vartype type: str + :ivar info: The additional info. + :vartype info: any + """ + + _attribute_map = { + 'type': {'key': 'type', 'type': 'str'}, + 'info': {'key': 'info', 'type': 'object'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword type: The additional info type. + :paramtype type: str + :keyword info: The additional info. + :paramtype info: any + """ + super(ErrorAdditionalInfo, self).__init__(**kwargs) + self.type = kwargs.get('type', None) + self.info = kwargs.get('info', None) + + +class ErrorResponse(msrest.serialization.Model): + """The error response. + + :ivar error: The root error. + :vartype error: ~azure.mgmt.machinelearningservices.models.RootError + :ivar correlation: Dictionary containing correlation details for the error. + :vartype correlation: dict[str, str] + :ivar environment: The hosting environment. + :vartype environment: str + :ivar location: The Azure region. + :vartype location: str + :ivar time: The time in UTC. + :vartype time: ~datetime.datetime + :ivar component_name: Component name where error originated/encountered. + :vartype component_name: str + """ + + _attribute_map = { + 'error': {'key': 'error', 'type': 'RootError'}, + 'correlation': {'key': 'correlation', 'type': '{str}'}, + 'environment': {'key': 'environment', 'type': 'str'}, + 'location': {'key': 'location', 'type': 'str'}, + 'time': {'key': 'time', 'type': 'iso-8601'}, + 'component_name': {'key': 'componentName', 'type': 'str'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword error: The root error. + :paramtype error: ~azure.mgmt.machinelearningservices.models.RootError + :keyword correlation: Dictionary containing correlation details for the error. + :paramtype correlation: dict[str, str] + :keyword environment: The hosting environment. + :paramtype environment: str + :keyword location: The Azure region. + :paramtype location: str + :keyword time: The time in UTC. + :paramtype time: ~datetime.datetime + :keyword component_name: Component name where error originated/encountered. + :paramtype component_name: str + """ + super(ErrorResponse, self).__init__(**kwargs) + self.error = kwargs.get('error', None) + self.correlation = kwargs.get('correlation', None) + self.environment = kwargs.get('environment', None) + self.location = kwargs.get('location', None) + self.time = kwargs.get('time', None) + self.component_name = kwargs.get('component_name', None) + + +class HistogramBin(msrest.serialization.Model): + """HistogramBin. + + :ivar lower_bound: + :vartype lower_bound: float + :ivar upper_bound: + :vartype upper_bound: float + :ivar count: + :vartype count: float + """ + + _attribute_map = { + 'lower_bound': {'key': 'lowerBound', 'type': 'float'}, + 'upper_bound': {'key': 'upperBound', 'type': 'float'}, + 'count': {'key': 'count', 'type': 'float'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword lower_bound: + :paramtype lower_bound: float + :keyword upper_bound: + :paramtype upper_bound: float + :keyword count: + :paramtype count: float + """ + super(HistogramBin, self).__init__(**kwargs) + self.lower_bound = kwargs.get('lower_bound', None) + self.upper_bound = kwargs.get('upper_bound', None) + self.count = kwargs.get('count', None) + + +class HttpContent(msrest.serialization.Model): + """HttpContent. + + Variables are only populated by the server, and will be ignored when sending a request. + + :ivar headers: + :vartype headers: + list[~azure.mgmt.machinelearningservices.models.KeyValuePairStringIEnumerable1] + """ + + _validation = { + 'headers': {'readonly': True}, + } + + _attribute_map = { + 'headers': {'key': 'headers', 'type': '[KeyValuePairStringIEnumerable1]'}, + } + + def __init__( + self, + **kwargs + ): + """ + """ + super(HttpContent, self).__init__(**kwargs) + self.headers = None + + +class HttpMethod(msrest.serialization.Model): + """HttpMethod. + + :ivar method: + :vartype method: str + """ + + _attribute_map = { + 'method': {'key': 'method', 'type': 'str'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword method: + :paramtype method: str + """ + super(HttpMethod, self).__init__(**kwargs) + self.method = kwargs.get('method', None) + + +class HttpRequestMessage(msrest.serialization.Model): + """HttpRequestMessage. + + Variables are only populated by the server, and will be ignored when sending a request. + + :ivar version: + :vartype version: str + :ivar version_policy: Possible values include: "RequestVersionOrLower", + "RequestVersionOrHigher", "RequestVersionExact". + :vartype version_policy: str or ~azure.mgmt.machinelearningservices.models.HttpVersionPolicy + :ivar content: + :vartype content: ~azure.mgmt.machinelearningservices.models.HttpContent + :ivar method: + :vartype method: ~azure.mgmt.machinelearningservices.models.HttpMethod + :ivar request_uri: + :vartype request_uri: str + :ivar headers: + :vartype headers: + list[~azure.mgmt.machinelearningservices.models.KeyValuePairStringIEnumerable1] + :ivar options: Dictionary of :code:`<any>`. + :vartype options: dict[str, any] + """ + + _validation = { + 'headers': {'readonly': True}, + 'options': {'readonly': True}, + } + + _attribute_map = { + 'version': {'key': 'version', 'type': 'str'}, + 'version_policy': {'key': 'versionPolicy', 'type': 'str'}, + 'content': {'key': 'content', 'type': 'HttpContent'}, + 'method': {'key': 'method', 'type': 'HttpMethod'}, + 'request_uri': {'key': 'requestUri', 'type': 'str'}, + 'headers': {'key': 'headers', 'type': '[KeyValuePairStringIEnumerable1]'}, + 'options': {'key': 'options', 'type': '{object}'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword version: + :paramtype version: str + :keyword version_policy: Possible values include: "RequestVersionOrLower", + "RequestVersionOrHigher", "RequestVersionExact". + :paramtype version_policy: str or ~azure.mgmt.machinelearningservices.models.HttpVersionPolicy + :keyword content: + :paramtype content: ~azure.mgmt.machinelearningservices.models.HttpContent + :keyword method: + :paramtype method: ~azure.mgmt.machinelearningservices.models.HttpMethod + :keyword request_uri: + :paramtype request_uri: str + """ + super(HttpRequestMessage, self).__init__(**kwargs) + self.version = kwargs.get('version', None) + self.version_policy = kwargs.get('version_policy', None) + self.content = kwargs.get('content', None) + self.method = kwargs.get('method', None) + self.request_uri = kwargs.get('request_uri', None) + self.headers = None + self.options = None + + +class HttpResponseMessage(msrest.serialization.Model): + """HttpResponseMessage. + + Variables are only populated by the server, and will be ignored when sending a request. + + :ivar version: + :vartype version: str + :ivar content: + :vartype content: ~azure.mgmt.machinelearningservices.models.HttpContent + :ivar status_code: Possible values include: "Continue", "SwitchingProtocols", "Processing", + "EarlyHints", "OK", "Created", "Accepted", "NonAuthoritativeInformation", "NoContent", + "ResetContent", "PartialContent", "MultiStatus", "AlreadyReported", "IMUsed", "Ambiguous", + "Moved", "Redirect", "RedirectMethod", "NotModified", "UseProxy", "Unused", + "TemporaryRedirect", "PermanentRedirect", "BadRequest", "Unauthorized", "PaymentRequired", + "Forbidden", "NotFound", "MethodNotAllowed", "NotAcceptable", "ProxyAuthenticationRequired", + "RequestTimeout", "Conflict", "Gone", "LengthRequired", "PreconditionFailed", + "RequestEntityTooLarge", "RequestUriTooLong", "UnsupportedMediaType", + "RequestedRangeNotSatisfiable", "ExpectationFailed", "MisdirectedRequest", + "UnprocessableEntity", "Locked", "FailedDependency", "UpgradeRequired", "PreconditionRequired", + "TooManyRequests", "RequestHeaderFieldsTooLarge", "UnavailableForLegalReasons", + "InternalServerError", "NotImplemented", "BadGateway", "ServiceUnavailable", "GatewayTimeout", + "HttpVersionNotSupported", "VariantAlsoNegotiates", "InsufficientStorage", "LoopDetected", + "NotExtended", "NetworkAuthenticationRequired". + :vartype status_code: str or ~azure.mgmt.machinelearningservices.models.HttpStatusCode + :ivar reason_phrase: + :vartype reason_phrase: str + :ivar headers: + :vartype headers: + list[~azure.mgmt.machinelearningservices.models.KeyValuePairStringIEnumerable1] + :ivar trailing_headers: + :vartype trailing_headers: + list[~azure.mgmt.machinelearningservices.models.KeyValuePairStringIEnumerable1] + :ivar request_message: + :vartype request_message: ~azure.mgmt.machinelearningservices.models.HttpRequestMessage + :ivar is_success_status_code: + :vartype is_success_status_code: bool + """ + + _validation = { + 'headers': {'readonly': True}, + 'trailing_headers': {'readonly': True}, + 'is_success_status_code': {'readonly': True}, + } + + _attribute_map = { + 'version': {'key': 'version', 'type': 'str'}, + 'content': {'key': 'content', 'type': 'HttpContent'}, + 'status_code': {'key': 'statusCode', 'type': 'str'}, + 'reason_phrase': {'key': 'reasonPhrase', 'type': 'str'}, + 'headers': {'key': 'headers', 'type': '[KeyValuePairStringIEnumerable1]'}, + 'trailing_headers': {'key': 'trailingHeaders', 'type': '[KeyValuePairStringIEnumerable1]'}, + 'request_message': {'key': 'requestMessage', 'type': 'HttpRequestMessage'}, + 'is_success_status_code': {'key': 'isSuccessStatusCode', 'type': 'bool'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword version: + :paramtype version: str + :keyword content: + :paramtype content: ~azure.mgmt.machinelearningservices.models.HttpContent + :keyword status_code: Possible values include: "Continue", "SwitchingProtocols", "Processing", + "EarlyHints", "OK", "Created", "Accepted", "NonAuthoritativeInformation", "NoContent", + "ResetContent", "PartialContent", "MultiStatus", "AlreadyReported", "IMUsed", "Ambiguous", + "Moved", "Redirect", "RedirectMethod", "NotModified", "UseProxy", "Unused", + "TemporaryRedirect", "PermanentRedirect", "BadRequest", "Unauthorized", "PaymentRequired", + "Forbidden", "NotFound", "MethodNotAllowed", "NotAcceptable", "ProxyAuthenticationRequired", + "RequestTimeout", "Conflict", "Gone", "LengthRequired", "PreconditionFailed", + "RequestEntityTooLarge", "RequestUriTooLong", "UnsupportedMediaType", + "RequestedRangeNotSatisfiable", "ExpectationFailed", "MisdirectedRequest", + "UnprocessableEntity", "Locked", "FailedDependency", "UpgradeRequired", "PreconditionRequired", + "TooManyRequests", "RequestHeaderFieldsTooLarge", "UnavailableForLegalReasons", + "InternalServerError", "NotImplemented", "BadGateway", "ServiceUnavailable", "GatewayTimeout", + "HttpVersionNotSupported", "VariantAlsoNegotiates", "InsufficientStorage", "LoopDetected", + "NotExtended", "NetworkAuthenticationRequired". + :paramtype status_code: str or ~azure.mgmt.machinelearningservices.models.HttpStatusCode + :keyword reason_phrase: + :paramtype reason_phrase: str + :keyword request_message: + :paramtype request_message: ~azure.mgmt.machinelearningservices.models.HttpRequestMessage + """ + super(HttpResponseMessage, self).__init__(**kwargs) + self.version = kwargs.get('version', None) + self.content = kwargs.get('content', None) + self.status_code = kwargs.get('status_code', None) + self.reason_phrase = kwargs.get('reason_phrase', None) + self.headers = None + self.trailing_headers = None + self.request_message = kwargs.get('request_message', None) + self.is_success_status_code = None + + +class InnerErrorResponse(msrest.serialization.Model): + """A nested structure of errors. + + :ivar code: The error code. + :vartype code: str + :ivar inner_error: A nested structure of errors. + :vartype inner_error: ~azure.mgmt.machinelearningservices.models.InnerErrorResponse + """ + + _attribute_map = { + 'code': {'key': 'code', 'type': 'str'}, + 'inner_error': {'key': 'innerError', 'type': 'InnerErrorResponse'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword code: The error code. + :paramtype code: str + :keyword inner_error: A nested structure of errors. + :paramtype inner_error: ~azure.mgmt.machinelearningservices.models.InnerErrorResponse + """ + super(InnerErrorResponse, self).__init__(**kwargs) + self.code = kwargs.get('code', None) + self.inner_error = kwargs.get('inner_error', None) + + +class KeyValuePairStringIEnumerable1(msrest.serialization.Model): + """KeyValuePairStringIEnumerable1. + + :ivar key: + :vartype key: str + :ivar value: + :vartype value: list[str] + """ + + _attribute_map = { + 'key': {'key': 'key', 'type': 'str'}, + 'value': {'key': 'value', 'type': '[str]'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword key: + :paramtype key: str + :keyword value: + :paramtype value: list[str] + """ + super(KeyValuePairStringIEnumerable1, self).__init__(**kwargs) + self.key = kwargs.get('key', None) + self.value = kwargs.get('value', None) + + +class LongRunningOperationResponse1LongRunningOperationResponseObject(msrest.serialization.Model): + """LongRunningOperationResponse1LongRunningOperationResponseObject. + + :ivar completion_result: Anything. + :vartype completion_result: any + :ivar location: + :vartype location: str + :ivar operation_result: + :vartype operation_result: str + """ + + _attribute_map = { + 'completion_result': {'key': 'completionResult', 'type': 'object'}, + 'location': {'key': 'location', 'type': 'str'}, + 'operation_result': {'key': 'operationResult', 'type': 'str'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword completion_result: Anything. + :paramtype completion_result: any + :keyword location: + :paramtype location: str + :keyword operation_result: + :paramtype operation_result: str + """ + super(LongRunningOperationResponse1LongRunningOperationResponseObject, self).__init__(**kwargs) + self.completion_result = kwargs.get('completion_result', None) + self.location = kwargs.get('location', None) + self.operation_result = kwargs.get('operation_result', None) + + +class Moments(msrest.serialization.Model): + """Moments. + + :ivar mean: + :vartype mean: float + :ivar standard_deviation: + :vartype standard_deviation: float + :ivar variance: + :vartype variance: float + :ivar skewness: + :vartype skewness: float + :ivar kurtosis: + :vartype kurtosis: float + """ + + _attribute_map = { + 'mean': {'key': 'mean', 'type': 'float'}, + 'standard_deviation': {'key': 'standardDeviation', 'type': 'float'}, + 'variance': {'key': 'variance', 'type': 'float'}, + 'skewness': {'key': 'skewness', 'type': 'float'}, + 'kurtosis': {'key': 'kurtosis', 'type': 'float'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword mean: + :paramtype mean: float + :keyword standard_deviation: + :paramtype standard_deviation: float + :keyword variance: + :paramtype variance: float + :keyword skewness: + :paramtype skewness: float + :keyword kurtosis: + :paramtype kurtosis: float + """ + super(Moments, self).__init__(**kwargs) + self.mean = kwargs.get('mean', None) + self.standard_deviation = kwargs.get('standard_deviation', None) + self.variance = kwargs.get('variance', None) + self.skewness = kwargs.get('skewness', None) + self.kurtosis = kwargs.get('kurtosis', None) + + +class PaginatedDataContainerEntityList(msrest.serialization.Model): + """A paginated list of DataContainerEntitys. + + :ivar value: An array of objects of type DataContainerEntity. + :vartype value: list[~azure.mgmt.machinelearningservices.models.DataContainerEntity] + :ivar continuation_token: The token used in retrieving the next page. If null, there are no + additional pages. + :vartype continuation_token: str + :ivar next_link: The link to the next page constructed using the continuationToken. If null, + there are no additional pages. + :vartype next_link: str + """ + + _attribute_map = { + 'value': {'key': 'value', 'type': '[DataContainerEntity]'}, + 'continuation_token': {'key': 'continuationToken', 'type': 'str'}, + 'next_link': {'key': 'nextLink', 'type': 'str'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword value: An array of objects of type DataContainerEntity. + :paramtype value: list[~azure.mgmt.machinelearningservices.models.DataContainerEntity] + :keyword continuation_token: The token used in retrieving the next page. If null, there are no + additional pages. + :paramtype continuation_token: str + :keyword next_link: The link to the next page constructed using the continuationToken. If + null, there are no additional pages. + :paramtype next_link: str + """ + super(PaginatedDataContainerEntityList, self).__init__(**kwargs) + self.value = kwargs.get('value', None) + self.continuation_token = kwargs.get('continuation_token', None) + self.next_link = kwargs.get('next_link', None) + + +class PaginatedDatasetDefinitionList(msrest.serialization.Model): + """A paginated list of DatasetDefinitions. + + :ivar value: An array of objects of type DatasetDefinition. + :vartype value: list[~azure.mgmt.machinelearningservices.models.DatasetDefinition] + :ivar continuation_token: The token used in retrieving the next page. If null, there are no + additional pages. + :vartype continuation_token: str + :ivar next_link: The link to the next page constructed using the continuationToken. If null, + there are no additional pages. + :vartype next_link: str + """ + + _attribute_map = { + 'value': {'key': 'value', 'type': '[DatasetDefinition]'}, + 'continuation_token': {'key': 'continuationToken', 'type': 'str'}, + 'next_link': {'key': 'nextLink', 'type': 'str'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword value: An array of objects of type DatasetDefinition. + :paramtype value: list[~azure.mgmt.machinelearningservices.models.DatasetDefinition] + :keyword continuation_token: The token used in retrieving the next page. If null, there are no + additional pages. + :paramtype continuation_token: str + :keyword next_link: The link to the next page constructed using the continuationToken. If + null, there are no additional pages. + :paramtype next_link: str + """ + super(PaginatedDatasetDefinitionList, self).__init__(**kwargs) + self.value = kwargs.get('value', None) + self.continuation_token = kwargs.get('continuation_token', None) + self.next_link = kwargs.get('next_link', None) + + +class PaginatedDatasetList(msrest.serialization.Model): + """A paginated list of Datasets. + + :ivar value: An array of objects of type Dataset. + :vartype value: list[~azure.mgmt.machinelearningservices.models.Dataset] + :ivar continuation_token: The token used in retrieving the next page. If null, there are no + additional pages. + :vartype continuation_token: str + :ivar next_link: The link to the next page constructed using the continuationToken. If null, + there are no additional pages. + :vartype next_link: str + """ + + _attribute_map = { + 'value': {'key': 'value', 'type': '[Dataset]'}, + 'continuation_token': {'key': 'continuationToken', 'type': 'str'}, + 'next_link': {'key': 'nextLink', 'type': 'str'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword value: An array of objects of type Dataset. + :paramtype value: list[~azure.mgmt.machinelearningservices.models.Dataset] + :keyword continuation_token: The token used in retrieving the next page. If null, there are no + additional pages. + :paramtype continuation_token: str + :keyword next_link: The link to the next page constructed using the continuationToken. If + null, there are no additional pages. + :paramtype next_link: str + """ + super(PaginatedDatasetList, self).__init__(**kwargs) + self.value = kwargs.get('value', None) + self.continuation_token = kwargs.get('continuation_token', None) + self.next_link = kwargs.get('next_link', None) + + +class PaginatedDatasetV2List(msrest.serialization.Model): + """A paginated list of DatasetV2s. + + :ivar value: An array of objects of type DatasetV2. + :vartype value: list[~azure.mgmt.machinelearningservices.models.DatasetV2] + :ivar continuation_token: The token used in retrieving the next page. If null, there are no + additional pages. + :vartype continuation_token: str + :ivar next_link: The link to the next page constructed using the continuationToken. If null, + there are no additional pages. + :vartype next_link: str + """ + + _attribute_map = { + 'value': {'key': 'value', 'type': '[DatasetV2]'}, + 'continuation_token': {'key': 'continuationToken', 'type': 'str'}, + 'next_link': {'key': 'nextLink', 'type': 'str'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword value: An array of objects of type DatasetV2. + :paramtype value: list[~azure.mgmt.machinelearningservices.models.DatasetV2] + :keyword continuation_token: The token used in retrieving the next page. If null, there are no + additional pages. + :paramtype continuation_token: str + :keyword next_link: The link to the next page constructed using the continuationToken. If + null, there are no additional pages. + :paramtype next_link: str + """ + super(PaginatedDatasetV2List, self).__init__(**kwargs) + self.value = kwargs.get('value', None) + self.continuation_token = kwargs.get('continuation_token', None) + self.next_link = kwargs.get('next_link', None) + + +class PaginatedDataVersionEntityList(msrest.serialization.Model): + """A paginated list of DataVersionEntitys. + + :ivar value: An array of objects of type DataVersionEntity. + :vartype value: list[~azure.mgmt.machinelearningservices.models.DataVersionEntity] + :ivar continuation_token: The token used in retrieving the next page. If null, there are no + additional pages. + :vartype continuation_token: str + :ivar next_link: The link to the next page constructed using the continuationToken. If null, + there are no additional pages. + :vartype next_link: str + """ + + _attribute_map = { + 'value': {'key': 'value', 'type': '[DataVersionEntity]'}, + 'continuation_token': {'key': 'continuationToken', 'type': 'str'}, + 'next_link': {'key': 'nextLink', 'type': 'str'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword value: An array of objects of type DataVersionEntity. + :paramtype value: list[~azure.mgmt.machinelearningservices.models.DataVersionEntity] + :keyword continuation_token: The token used in retrieving the next page. If null, there are no + additional pages. + :paramtype continuation_token: str + :keyword next_link: The link to the next page constructed using the continuationToken. If + null, there are no additional pages. + :paramtype next_link: str + """ + super(PaginatedDataVersionEntityList, self).__init__(**kwargs) + self.value = kwargs.get('value', None) + self.continuation_token = kwargs.get('continuation_token', None) + self.next_link = kwargs.get('next_link', None) + + +class PaginatedStringList(msrest.serialization.Model): + """A paginated list of Strings. + + :ivar value: An array of objects of type String. + :vartype value: list[str] + :ivar continuation_token: The token used in retrieving the next page. If null, there are no + additional pages. + :vartype continuation_token: str + :ivar next_link: The link to the next page constructed using the continuationToken. If null, + there are no additional pages. + :vartype next_link: str + """ + + _attribute_map = { + 'value': {'key': 'value', 'type': '[str]'}, + 'continuation_token': {'key': 'continuationToken', 'type': 'str'}, + 'next_link': {'key': 'nextLink', 'type': 'str'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword value: An array of objects of type String. + :paramtype value: list[str] + :keyword continuation_token: The token used in retrieving the next page. If null, there are no + additional pages. + :paramtype continuation_token: str + :keyword next_link: The link to the next page constructed using the continuationToken. If + null, there are no additional pages. + :paramtype next_link: str + """ + super(PaginatedStringList, self).__init__(**kwargs) + self.value = kwargs.get('value', None) + self.continuation_token = kwargs.get('continuation_token', None) + self.next_link = kwargs.get('next_link', None) + + +class ProfileActionResult(msrest.serialization.Model): + """ProfileActionResult. + + :ivar profile_action_id: + :vartype profile_action_id: str + :ivar status: + :vartype status: str + :ivar completed_on_utc: + :vartype completed_on_utc: ~datetime.datetime + :ivar action_result: + :vartype action_result: ~azure.mgmt.machinelearningservices.models.ActionResult + """ + + _attribute_map = { + 'profile_action_id': {'key': 'profileActionId', 'type': 'str'}, + 'status': {'key': 'status', 'type': 'str'}, + 'completed_on_utc': {'key': 'completedOnUtc', 'type': 'iso-8601'}, + 'action_result': {'key': 'actionResult', 'type': 'ActionResult'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword profile_action_id: + :paramtype profile_action_id: str + :keyword status: + :paramtype status: str + :keyword completed_on_utc: + :paramtype completed_on_utc: ~datetime.datetime + :keyword action_result: + :paramtype action_result: ~azure.mgmt.machinelearningservices.models.ActionResult + """ + super(ProfileActionResult, self).__init__(**kwargs) + self.profile_action_id = kwargs.get('profile_action_id', None) + self.status = kwargs.get('status', None) + self.completed_on_utc = kwargs.get('completed_on_utc', None) + self.action_result = kwargs.get('action_result', None) + + +class ProfileResult(msrest.serialization.Model): + """ProfileResult. + + :ivar column_name: + :vartype column_name: str + :ivar type: Possible values include: "String", "Boolean", "Integer", "Decimal", "Date", + "Unknown", "Error", "Null", "DataRow", "List", "Stream". + :vartype type: str or ~azure.mgmt.machinelearningservices.models.FieldType + :ivar min: + :vartype min: ~azure.mgmt.machinelearningservices.models.DataField + :ivar max: + :vartype max: ~azure.mgmt.machinelearningservices.models.DataField + :ivar count: + :vartype count: long + :ivar missing_count: + :vartype missing_count: long + :ivar not_missing_count: + :vartype not_missing_count: long + :ivar percent_missing: + :vartype percent_missing: float + :ivar error_count: + :vartype error_count: long + :ivar empty_count: + :vartype empty_count: long + :ivar quantiles: + :vartype quantiles: ~azure.mgmt.machinelearningservices.models.Quantiles + :ivar whisker_top: + :vartype whisker_top: float + :ivar whisker_bottom: + :vartype whisker_bottom: float + :ivar moments: + :vartype moments: ~azure.mgmt.machinelearningservices.models.Moments + :ivar type_counts: + :vartype type_counts: list[~azure.mgmt.machinelearningservices.models.TypeCount] + :ivar value_counts: + :vartype value_counts: list[~azure.mgmt.machinelearningservices.models.ValueCount] + :ivar unique_values: + :vartype unique_values: long + :ivar histogram: + :vartype histogram: list[~azure.mgmt.machinelearningservices.models.HistogramBin] + :ivar s_type_counts: + :vartype s_type_counts: list[~azure.mgmt.machinelearningservices.models.STypeCount] + :ivar average_spaces_count: + :vartype average_spaces_count: float + :ivar string_lengths: + :vartype string_lengths: list[~azure.mgmt.machinelearningservices.models.StringLengthCount] + """ + + _attribute_map = { + 'column_name': {'key': 'columnName', 'type': 'str'}, + 'type': {'key': 'type', 'type': 'str'}, + 'min': {'key': 'min', 'type': 'DataField'}, + 'max': {'key': 'max', 'type': 'DataField'}, + 'count': {'key': 'count', 'type': 'long'}, + 'missing_count': {'key': 'missingCount', 'type': 'long'}, + 'not_missing_count': {'key': 'notMissingCount', 'type': 'long'}, + 'percent_missing': {'key': 'percentMissing', 'type': 'float'}, + 'error_count': {'key': 'errorCount', 'type': 'long'}, + 'empty_count': {'key': 'emptyCount', 'type': 'long'}, + 'quantiles': {'key': 'quantiles', 'type': 'Quantiles'}, + 'whisker_top': {'key': 'whiskerTop', 'type': 'float'}, + 'whisker_bottom': {'key': 'whiskerBottom', 'type': 'float'}, + 'moments': {'key': 'moments', 'type': 'Moments'}, + 'type_counts': {'key': 'typeCounts', 'type': '[TypeCount]'}, + 'value_counts': {'key': 'valueCounts', 'type': '[ValueCount]'}, + 'unique_values': {'key': 'uniqueValues', 'type': 'long'}, + 'histogram': {'key': 'histogram', 'type': '[HistogramBin]'}, + 's_type_counts': {'key': 'sTypeCounts', 'type': '[STypeCount]'}, + 'average_spaces_count': {'key': 'averageSpacesCount', 'type': 'float'}, + 'string_lengths': {'key': 'stringLengths', 'type': '[StringLengthCount]'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword column_name: + :paramtype column_name: str + :keyword type: Possible values include: "String", "Boolean", "Integer", "Decimal", "Date", + "Unknown", "Error", "Null", "DataRow", "List", "Stream". + :paramtype type: str or ~azure.mgmt.machinelearningservices.models.FieldType + :keyword min: + :paramtype min: ~azure.mgmt.machinelearningservices.models.DataField + :keyword max: + :paramtype max: ~azure.mgmt.machinelearningservices.models.DataField + :keyword count: + :paramtype count: long + :keyword missing_count: + :paramtype missing_count: long + :keyword not_missing_count: + :paramtype not_missing_count: long + :keyword percent_missing: + :paramtype percent_missing: float + :keyword error_count: + :paramtype error_count: long + :keyword empty_count: + :paramtype empty_count: long + :keyword quantiles: + :paramtype quantiles: ~azure.mgmt.machinelearningservices.models.Quantiles + :keyword whisker_top: + :paramtype whisker_top: float + :keyword whisker_bottom: + :paramtype whisker_bottom: float + :keyword moments: + :paramtype moments: ~azure.mgmt.machinelearningservices.models.Moments + :keyword type_counts: + :paramtype type_counts: list[~azure.mgmt.machinelearningservices.models.TypeCount] + :keyword value_counts: + :paramtype value_counts: list[~azure.mgmt.machinelearningservices.models.ValueCount] + :keyword unique_values: + :paramtype unique_values: long + :keyword histogram: + :paramtype histogram: list[~azure.mgmt.machinelearningservices.models.HistogramBin] + :keyword s_type_counts: + :paramtype s_type_counts: list[~azure.mgmt.machinelearningservices.models.STypeCount] + :keyword average_spaces_count: + :paramtype average_spaces_count: float + :keyword string_lengths: + :paramtype string_lengths: list[~azure.mgmt.machinelearningservices.models.StringLengthCount] + """ + super(ProfileResult, self).__init__(**kwargs) + self.column_name = kwargs.get('column_name', None) + self.type = kwargs.get('type', None) + self.min = kwargs.get('min', None) + self.max = kwargs.get('max', None) + self.count = kwargs.get('count', None) + self.missing_count = kwargs.get('missing_count', None) + self.not_missing_count = kwargs.get('not_missing_count', None) + self.percent_missing = kwargs.get('percent_missing', None) + self.error_count = kwargs.get('error_count', None) + self.empty_count = kwargs.get('empty_count', None) + self.quantiles = kwargs.get('quantiles', None) + self.whisker_top = kwargs.get('whisker_top', None) + self.whisker_bottom = kwargs.get('whisker_bottom', None) + self.moments = kwargs.get('moments', None) + self.type_counts = kwargs.get('type_counts', None) + self.value_counts = kwargs.get('value_counts', None) + self.unique_values = kwargs.get('unique_values', None) + self.histogram = kwargs.get('histogram', None) + self.s_type_counts = kwargs.get('s_type_counts', None) + self.average_spaces_count = kwargs.get('average_spaces_count', None) + self.string_lengths = kwargs.get('string_lengths', None) + + +class Quantiles(msrest.serialization.Model): + """Quantiles. + + :ivar p0_d1: + :vartype p0_d1: float + :ivar p1: + :vartype p1: float + :ivar p5: + :vartype p5: float + :ivar p25: + :vartype p25: float + :ivar p50: + :vartype p50: float + :ivar p75: + :vartype p75: float + :ivar p95: + :vartype p95: float + :ivar p99: + :vartype p99: float + :ivar p99_d9: + :vartype p99_d9: float + """ + + _attribute_map = { + 'p0_d1': {'key': 'p0D1', 'type': 'float'}, + 'p1': {'key': 'p1', 'type': 'float'}, + 'p5': {'key': 'p5', 'type': 'float'}, + 'p25': {'key': 'p25', 'type': 'float'}, + 'p50': {'key': 'p50', 'type': 'float'}, + 'p75': {'key': 'p75', 'type': 'float'}, + 'p95': {'key': 'p95', 'type': 'float'}, + 'p99': {'key': 'p99', 'type': 'float'}, + 'p99_d9': {'key': 'p99D9', 'type': 'float'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword p0_d1: + :paramtype p0_d1: float + :keyword p1: + :paramtype p1: float + :keyword p5: + :paramtype p5: float + :keyword p25: + :paramtype p25: float + :keyword p50: + :paramtype p50: float + :keyword p75: + :paramtype p75: float + :keyword p95: + :paramtype p95: float + :keyword p99: + :paramtype p99: float + :keyword p99_d9: + :paramtype p99_d9: float + """ + super(Quantiles, self).__init__(**kwargs) + self.p0_d1 = kwargs.get('p0_d1', None) + self.p1 = kwargs.get('p1', None) + self.p5 = kwargs.get('p5', None) + self.p25 = kwargs.get('p25', None) + self.p50 = kwargs.get('p50', None) + self.p75 = kwargs.get('p75', None) + self.p95 = kwargs.get('p95', None) + self.p99 = kwargs.get('p99', None) + self.p99_d9 = kwargs.get('p99_d9', None) + + +class RegisterExistingData(msrest.serialization.Model): + """RegisterExistingData. + + All required parameters must be populated in order to send to Azure. + + :ivar existing_unregistered_asset_id: Required. + :vartype existing_unregistered_asset_id: str + :ivar name: Required. + :vartype name: str + :ivar version: + :vartype version: str + """ + + _validation = { + 'existing_unregistered_asset_id': {'required': True}, + 'name': {'required': True}, + } + + _attribute_map = { + 'existing_unregistered_asset_id': {'key': 'existingUnregisteredAssetId', 'type': 'str'}, + 'name': {'key': 'name', 'type': 'str'}, + 'version': {'key': 'version', 'type': 'str'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword existing_unregistered_asset_id: Required. + :paramtype existing_unregistered_asset_id: str + :keyword name: Required. + :paramtype name: str + :keyword version: + :paramtype version: str + """ + super(RegisterExistingData, self).__init__(**kwargs) + self.existing_unregistered_asset_id = kwargs['existing_unregistered_asset_id'] + self.name = kwargs['name'] + self.version = kwargs.get('version', None) + + +class RootError(msrest.serialization.Model): + """The root error. + + :ivar code: The service-defined error code. Supported error codes: ServiceError, UserError, + ValidationError, AzureStorageError, TransientError, RequestThrottled. + :vartype code: str + :ivar severity: The Severity of error. + :vartype severity: int + :ivar message: A human-readable representation of the error. + :vartype message: str + :ivar message_format: An unformatted version of the message with no variable substitution. + :vartype message_format: str + :ivar message_parameters: Value substitutions corresponding to the contents of MessageFormat. + :vartype message_parameters: dict[str, str] + :ivar reference_code: This code can optionally be set by the system generating the error. + It should be used to classify the problem and identify the module and code area where the + failure occured. + :vartype reference_code: str + :ivar details_uri: A URI which points to more details about the context of the error. + :vartype details_uri: str + :ivar target: The target of the error (e.g., the name of the property in error). + :vartype target: str + :ivar details: The related errors that occurred during the request. + :vartype details: list[~azure.mgmt.machinelearningservices.models.RootError] + :ivar inner_error: A nested structure of errors. + :vartype inner_error: ~azure.mgmt.machinelearningservices.models.InnerErrorResponse + :ivar additional_info: The error additional info. + :vartype additional_info: list[~azure.mgmt.machinelearningservices.models.ErrorAdditionalInfo] + """ + + _attribute_map = { + 'code': {'key': 'code', 'type': 'str'}, + 'severity': {'key': 'severity', 'type': 'int'}, + 'message': {'key': 'message', 'type': 'str'}, + 'message_format': {'key': 'messageFormat', 'type': 'str'}, + 'message_parameters': {'key': 'messageParameters', 'type': '{str}'}, + 'reference_code': {'key': 'referenceCode', 'type': 'str'}, + 'details_uri': {'key': 'detailsUri', 'type': 'str'}, + 'target': {'key': 'target', 'type': 'str'}, + 'details': {'key': 'details', 'type': '[RootError]'}, + 'inner_error': {'key': 'innerError', 'type': 'InnerErrorResponse'}, + 'additional_info': {'key': 'additionalInfo', 'type': '[ErrorAdditionalInfo]'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword code: The service-defined error code. Supported error codes: ServiceError, UserError, + ValidationError, AzureStorageError, TransientError, RequestThrottled. + :paramtype code: str + :keyword severity: The Severity of error. + :paramtype severity: int + :keyword message: A human-readable representation of the error. + :paramtype message: str + :keyword message_format: An unformatted version of the message with no variable substitution. + :paramtype message_format: str + :keyword message_parameters: Value substitutions corresponding to the contents of + MessageFormat. + :paramtype message_parameters: dict[str, str] + :keyword reference_code: This code can optionally be set by the system generating the error. + It should be used to classify the problem and identify the module and code area where the + failure occured. + :paramtype reference_code: str + :keyword details_uri: A URI which points to more details about the context of the error. + :paramtype details_uri: str + :keyword target: The target of the error (e.g., the name of the property in error). + :paramtype target: str + :keyword details: The related errors that occurred during the request. + :paramtype details: list[~azure.mgmt.machinelearningservices.models.RootError] + :keyword inner_error: A nested structure of errors. + :paramtype inner_error: ~azure.mgmt.machinelearningservices.models.InnerErrorResponse + :keyword additional_info: The error additional info. + :paramtype additional_info: + list[~azure.mgmt.machinelearningservices.models.ErrorAdditionalInfo] + """ + super(RootError, self).__init__(**kwargs) + self.code = kwargs.get('code', None) + self.severity = kwargs.get('severity', None) + self.message = kwargs.get('message', None) + self.message_format = kwargs.get('message_format', None) + self.message_parameters = kwargs.get('message_parameters', None) + self.reference_code = kwargs.get('reference_code', None) + self.details_uri = kwargs.get('details_uri', None) + self.target = kwargs.get('target', None) + self.details = kwargs.get('details', None) + self.inner_error = kwargs.get('inner_error', None) + self.additional_info = kwargs.get('additional_info', None) + + +class SqlDataPath(msrest.serialization.Model): + """SqlDataPath. + + :ivar sql_table_name: + :vartype sql_table_name: str + :ivar sql_query: + :vartype sql_query: str + :ivar sql_stored_procedure_name: + :vartype sql_stored_procedure_name: str + :ivar sql_stored_procedure_params: + :vartype sql_stored_procedure_params: + list[~azure.mgmt.machinelearningservices.models.StoredProcedureParameter] + :ivar query_timeout: + :vartype query_timeout: long + """ + + _attribute_map = { + 'sql_table_name': {'key': 'sqlTableName', 'type': 'str'}, + 'sql_query': {'key': 'sqlQuery', 'type': 'str'}, + 'sql_stored_procedure_name': {'key': 'sqlStoredProcedureName', 'type': 'str'}, + 'sql_stored_procedure_params': {'key': 'sqlStoredProcedureParams', 'type': '[StoredProcedureParameter]'}, + 'query_timeout': {'key': 'queryTimeout', 'type': 'long'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword sql_table_name: + :paramtype sql_table_name: str + :keyword sql_query: + :paramtype sql_query: str + :keyword sql_stored_procedure_name: + :paramtype sql_stored_procedure_name: str + :keyword sql_stored_procedure_params: + :paramtype sql_stored_procedure_params: + list[~azure.mgmt.machinelearningservices.models.StoredProcedureParameter] + :keyword query_timeout: + :paramtype query_timeout: long + """ + super(SqlDataPath, self).__init__(**kwargs) + self.sql_table_name = kwargs.get('sql_table_name', None) + self.sql_query = kwargs.get('sql_query', None) + self.sql_stored_procedure_name = kwargs.get('sql_stored_procedure_name', None) + self.sql_stored_procedure_params = kwargs.get('sql_stored_procedure_params', None) + self.query_timeout = kwargs.get('query_timeout', None) + + +class StoredProcedureParameter(msrest.serialization.Model): + """StoredProcedureParameter. + + :ivar name: + :vartype name: str + :ivar value: + :vartype value: str + :ivar type: Possible values include: "String", "Int", "Decimal", "Guid", "Boolean", "Date". + :vartype type: str or ~azure.mgmt.machinelearningservices.models.StoredProcedureParameterType + """ + + _attribute_map = { + 'name': {'key': 'name', 'type': 'str'}, + 'value': {'key': 'value', 'type': 'str'}, + 'type': {'key': 'type', 'type': 'str'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword name: + :paramtype name: str + :keyword value: + :paramtype value: str + :keyword type: Possible values include: "String", "Int", "Decimal", "Guid", "Boolean", "Date". + :paramtype type: str or ~azure.mgmt.machinelearningservices.models.StoredProcedureParameterType + """ + super(StoredProcedureParameter, self).__init__(**kwargs) + self.name = kwargs.get('name', None) + self.value = kwargs.get('value', None) + self.type = kwargs.get('type', None) + + +class StringLengthCount(msrest.serialization.Model): + """StringLengthCount. + + :ivar length: + :vartype length: long + :ivar count: + :vartype count: long + """ + + _attribute_map = { + 'length': {'key': 'length', 'type': 'long'}, + 'count': {'key': 'count', 'type': 'long'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword length: + :paramtype length: long + :keyword count: + :paramtype count: long + """ + super(StringLengthCount, self).__init__(**kwargs) + self.length = kwargs.get('length', None) + self.count = kwargs.get('count', None) + + +class STypeCount(msrest.serialization.Model): + """STypeCount. + + :ivar s_type: Possible values include: "EmailAddress", "GeographicCoordinate", "Ipv4Address", + "Ipv6Address", "UsPhoneNumber", "ZipCode". + :vartype s_type: str or ~azure.mgmt.machinelearningservices.models.SType + :ivar count: + :vartype count: long + """ + + _attribute_map = { + 's_type': {'key': 'sType', 'type': 'str'}, + 'count': {'key': 'count', 'type': 'long'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword s_type: Possible values include: "EmailAddress", "GeographicCoordinate", + "Ipv4Address", "Ipv6Address", "UsPhoneNumber", "ZipCode". + :paramtype s_type: str or ~azure.mgmt.machinelearningservices.models.SType + :keyword count: + :paramtype count: long + """ + super(STypeCount, self).__init__(**kwargs) + self.s_type = kwargs.get('s_type', None) + self.count = kwargs.get('count', None) + + +class TypeCount(msrest.serialization.Model): + """TypeCount. + + :ivar type: Possible values include: "String", "Boolean", "Integer", "Decimal", "Date", + "Unknown", "Error", "Null", "DataRow", "List", "Stream". + :vartype type: str or ~azure.mgmt.machinelearningservices.models.FieldType + :ivar count: + :vartype count: long + """ + + _attribute_map = { + 'type': {'key': 'type', 'type': 'str'}, + 'count': {'key': 'count', 'type': 'long'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword type: Possible values include: "String", "Boolean", "Integer", "Decimal", "Date", + "Unknown", "Error", "Null", "DataRow", "List", "Stream". + :paramtype type: str or ~azure.mgmt.machinelearningservices.models.FieldType + :keyword count: + :paramtype count: long + """ + super(TypeCount, self).__init__(**kwargs) + self.type = kwargs.get('type', None) + self.count = kwargs.get('count', None) + + +class User(msrest.serialization.Model): + """User. + + :ivar user_object_id: A user or service principal's object ID. + This is EUPI and may only be logged to warm path telemetry. + :vartype user_object_id: str + :ivar user_pu_id: A user or service principal's PuID. + This is PII and should never be logged. + :vartype user_pu_id: str + :ivar user_idp: A user identity provider. Eg live.com + This is PII and should never be logged. + :vartype user_idp: str + :ivar user_alt_sec_id: A user alternate sec id. This represents the user in a different + identity provider system Eg.1:live.com:puid + This is PII and should never be logged. + :vartype user_alt_sec_id: str + :ivar user_iss: The issuer which issed the token for this user. + This is PII and should never be logged. + :vartype user_iss: str + :ivar user_tenant_id: A user or service principal's tenant ID. + :vartype user_tenant_id: str + :ivar user_name: A user's full name or a service principal's app ID. + This is PII and should never be logged. + :vartype user_name: str + :ivar upn: A user's Principal name (upn) + This is PII andshould never be logged. + :vartype upn: str + """ + + _attribute_map = { + 'user_object_id': {'key': 'userObjectId', 'type': 'str'}, + 'user_pu_id': {'key': 'userPuId', 'type': 'str'}, + 'user_idp': {'key': 'userIdp', 'type': 'str'}, + 'user_alt_sec_id': {'key': 'userAltSecId', 'type': 'str'}, + 'user_iss': {'key': 'userIss', 'type': 'str'}, + 'user_tenant_id': {'key': 'userTenantId', 'type': 'str'}, + 'user_name': {'key': 'userName', 'type': 'str'}, + 'upn': {'key': 'upn', 'type': 'str'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword user_object_id: A user or service principal's object ID. + This is EUPI and may only be logged to warm path telemetry. + :paramtype user_object_id: str + :keyword user_pu_id: A user or service principal's PuID. + This is PII and should never be logged. + :paramtype user_pu_id: str + :keyword user_idp: A user identity provider. Eg live.com + This is PII and should never be logged. + :paramtype user_idp: str + :keyword user_alt_sec_id: A user alternate sec id. This represents the user in a different + identity provider system Eg.1:live.com:puid + This is PII and should never be logged. + :paramtype user_alt_sec_id: str + :keyword user_iss: The issuer which issed the token for this user. + This is PII and should never be logged. + :paramtype user_iss: str + :keyword user_tenant_id: A user or service principal's tenant ID. + :paramtype user_tenant_id: str + :keyword user_name: A user's full name or a service principal's app ID. + This is PII and should never be logged. + :paramtype user_name: str + :keyword upn: A user's Principal name (upn) + This is PII andshould never be logged. + :paramtype upn: str + """ + super(User, self).__init__(**kwargs) + self.user_object_id = kwargs.get('user_object_id', None) + self.user_pu_id = kwargs.get('user_pu_id', None) + self.user_idp = kwargs.get('user_idp', None) + self.user_alt_sec_id = kwargs.get('user_alt_sec_id', None) + self.user_iss = kwargs.get('user_iss', None) + self.user_tenant_id = kwargs.get('user_tenant_id', None) + self.user_name = kwargs.get('user_name', None) + self.upn = kwargs.get('upn', None) + + +class ValueCount(msrest.serialization.Model): + """ValueCount. + + :ivar value: + :vartype value: ~azure.mgmt.machinelearningservices.models.DataField + :ivar count: + :vartype count: long + """ + + _attribute_map = { + 'value': {'key': 'value', 'type': 'DataField'}, + 'count': {'key': 'count', 'type': 'long'}, + } + + def __init__( + self, + **kwargs + ): + """ + :keyword value: + :paramtype value: ~azure.mgmt.machinelearningservices.models.DataField + :keyword count: + :paramtype count: long + """ + super(ValueCount, self).__init__(**kwargs) + self.value = kwargs.get('value', None) + self.count = kwargs.get('count', None) diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/models/_models_py3.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/models/_models_py3.py new file mode 100644 index 00000000..159fd205 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/models/_models_py3.py @@ -0,0 +1,2916 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) AutoRest Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +import datetime +from typing import Any, Dict, List, Optional, Union + +from azure.core.exceptions import HttpResponseError +import msrest.serialization + +from ._azure_machine_learning_workspaces_enums import * + + +class ActionResult(msrest.serialization.Model): + """ActionResult. + + :ivar is_up_to_date: + :vartype is_up_to_date: bool + :ivar is_up_to_date_error: + :vartype is_up_to_date_error: str + :ivar result_artifact_ids: + :vartype result_artifact_ids: list[str] + :ivar in_progress_action_id: + :vartype in_progress_action_id: str + :ivar run_id: + :vartype run_id: str + :ivar experiment_name: + :vartype experiment_name: str + :ivar datastore_name: + :vartype datastore_name: str + """ + + _attribute_map = { + 'is_up_to_date': {'key': 'isUpToDate', 'type': 'bool'}, + 'is_up_to_date_error': {'key': 'isUpToDateError', 'type': 'str'}, + 'result_artifact_ids': {'key': 'resultArtifactIds', 'type': '[str]'}, + 'in_progress_action_id': {'key': 'inProgressActionId', 'type': 'str'}, + 'run_id': {'key': 'runId', 'type': 'str'}, + 'experiment_name': {'key': 'experimentName', 'type': 'str'}, + 'datastore_name': {'key': 'datastoreName', 'type': 'str'}, + } + + def __init__( + self, + *, + is_up_to_date: Optional[bool] = None, + is_up_to_date_error: Optional[str] = None, + result_artifact_ids: Optional[List[str]] = None, + in_progress_action_id: Optional[str] = None, + run_id: Optional[str] = None, + experiment_name: Optional[str] = None, + datastore_name: Optional[str] = None, + **kwargs + ): + """ + :keyword is_up_to_date: + :paramtype is_up_to_date: bool + :keyword is_up_to_date_error: + :paramtype is_up_to_date_error: str + :keyword result_artifact_ids: + :paramtype result_artifact_ids: list[str] + :keyword in_progress_action_id: + :paramtype in_progress_action_id: str + :keyword run_id: + :paramtype run_id: str + :keyword experiment_name: + :paramtype experiment_name: str + :keyword datastore_name: + :paramtype datastore_name: str + """ + super(ActionResult, self).__init__(**kwargs) + self.is_up_to_date = is_up_to_date + self.is_up_to_date_error = is_up_to_date_error + self.result_artifact_ids = result_artifact_ids + self.in_progress_action_id = in_progress_action_id + self.run_id = run_id + self.experiment_name = experiment_name + self.datastore_name = datastore_name + + +class AssetId(msrest.serialization.Model): + """AssetId. + + All required parameters must be populated in order to send to Azure. + + :ivar value: Required. + :vartype value: str + """ + + _validation = { + 'value': {'required': True}, + } + + _attribute_map = { + 'value': {'key': 'value', 'type': 'str'}, + } + + def __init__( + self, + *, + value: str, + **kwargs + ): + """ + :keyword value: Required. + :paramtype value: str + """ + super(AssetId, self).__init__(**kwargs) + self.value = value + + +class BatchDataUriResponse(msrest.serialization.Model): + """BatchDataUriResponse. + + :ivar values: Dictionary of :code:`<DataUriV2Response>`. + :vartype values: dict[str, ~azure.mgmt.machinelearningservices.models.DataUriV2Response] + """ + + _attribute_map = { + 'values': {'key': 'values', 'type': '{DataUriV2Response}'}, + } + + def __init__( + self, + *, + values: Optional[Dict[str, "DataUriV2Response"]] = None, + **kwargs + ): + """ + :keyword values: Dictionary of :code:`<DataUriV2Response>`. + :paramtype values: dict[str, ~azure.mgmt.machinelearningservices.models.DataUriV2Response] + """ + super(BatchDataUriResponse, self).__init__(**kwargs) + self.values = values + + +class BatchGetResolvedURIs(msrest.serialization.Model): + """BatchGetResolvedURIs. + + All required parameters must be populated in order to send to Azure. + + :ivar values: Required. + :vartype values: list[str] + """ + + _validation = { + 'values': {'required': True}, + } + + _attribute_map = { + 'values': {'key': 'values', 'type': '[str]'}, + } + + def __init__( + self, + *, + values: List[str], + **kwargs + ): + """ + :keyword values: Required. + :paramtype values: list[str] + """ + super(BatchGetResolvedURIs, self).__init__(**kwargs) + self.values = values + + +class ColumnDefinition(msrest.serialization.Model): + """ColumnDefinition. + + :ivar id: + :vartype id: str + :ivar type: Possible values include: "String", "Boolean", "Integer", "Decimal", "Date", + "Unknown", "Error", "Null", "DataRow", "List", "Stream". + :vartype type: str or ~azure.mgmt.machinelearningservices.models.FieldType + """ + + _attribute_map = { + 'id': {'key': 'id', 'type': 'str'}, + 'type': {'key': 'type', 'type': 'str'}, + } + + def __init__( + self, + *, + id: Optional[str] = None, + type: Optional[Union[str, "FieldType"]] = None, + **kwargs + ): + """ + :keyword id: + :paramtype id: str + :keyword type: Possible values include: "String", "Boolean", "Integer", "Decimal", "Date", + "Unknown", "Error", "Null", "DataRow", "List", "Stream". + :paramtype type: str or ~azure.mgmt.machinelearningservices.models.FieldType + """ + super(ColumnDefinition, self).__init__(**kwargs) + self.id = id + self.type = type + + +class CreateUnregisteredInputData(msrest.serialization.Model): + """CreateUnregisteredInputData. + + All required parameters must be populated in order to send to Azure. + + :ivar run_id: Required. + :vartype run_id: str + :ivar input_name: Required. + :vartype input_name: str + :ivar uri: Required. + :vartype uri: str + :ivar type: Required. + :vartype type: str + """ + + _validation = { + 'run_id': {'required': True}, + 'input_name': {'required': True}, + 'uri': {'required': True}, + 'type': {'required': True}, + } + + _attribute_map = { + 'run_id': {'key': 'runId', 'type': 'str'}, + 'input_name': {'key': 'inputName', 'type': 'str'}, + 'uri': {'key': 'uri', 'type': 'str'}, + 'type': {'key': 'type', 'type': 'str'}, + } + + def __init__( + self, + *, + run_id: str, + input_name: str, + uri: str, + type: str, + **kwargs + ): + """ + :keyword run_id: Required. + :paramtype run_id: str + :keyword input_name: Required. + :paramtype input_name: str + :keyword uri: Required. + :paramtype uri: str + :keyword type: Required. + :paramtype type: str + """ + super(CreateUnregisteredInputData, self).__init__(**kwargs) + self.run_id = run_id + self.input_name = input_name + self.uri = uri + self.type = type + + +class CreateUnregisteredOutputData(msrest.serialization.Model): + """CreateUnregisteredOutputData. + + All required parameters must be populated in order to send to Azure. + + :ivar run_id: Required. + :vartype run_id: str + :ivar output_name: Required. + :vartype output_name: str + :ivar uri: Required. + :vartype uri: str + :ivar type: Required. + :vartype type: str + """ + + _validation = { + 'run_id': {'required': True}, + 'output_name': {'required': True}, + 'uri': {'required': True}, + 'type': {'required': True}, + } + + _attribute_map = { + 'run_id': {'key': 'runId', 'type': 'str'}, + 'output_name': {'key': 'outputName', 'type': 'str'}, + 'uri': {'key': 'uri', 'type': 'str'}, + 'type': {'key': 'type', 'type': 'str'}, + } + + def __init__( + self, + *, + run_id: str, + output_name: str, + uri: str, + type: str, + **kwargs + ): + """ + :keyword run_id: Required. + :paramtype run_id: str + :keyword output_name: Required. + :paramtype output_name: str + :keyword uri: Required. + :paramtype uri: str + :keyword type: Required. + :paramtype type: str + """ + super(CreateUnregisteredOutputData, self).__init__(**kwargs) + self.run_id = run_id + self.output_name = output_name + self.uri = uri + self.type = type + + +class DataCallRequest(msrest.serialization.Model): + """DataCallRequest. + + :ivar data_uri: + :vartype data_uri: str + :ivar data_type: + :vartype data_type: str + :ivar asset_id: + :vartype asset_id: str + :ivar data_container_name: + :vartype data_container_name: str + :ivar version_id: + :vartype version_id: str + """ + + _attribute_map = { + 'data_uri': {'key': 'dataUri', 'type': 'str'}, + 'data_type': {'key': 'dataType', 'type': 'str'}, + 'asset_id': {'key': 'assetId', 'type': 'str'}, + 'data_container_name': {'key': 'dataContainerName', 'type': 'str'}, + 'version_id': {'key': 'versionId', 'type': 'str'}, + } + + def __init__( + self, + *, + data_uri: Optional[str] = None, + data_type: Optional[str] = None, + asset_id: Optional[str] = None, + data_container_name: Optional[str] = None, + version_id: Optional[str] = None, + **kwargs + ): + """ + :keyword data_uri: + :paramtype data_uri: str + :keyword data_type: + :paramtype data_type: str + :keyword asset_id: + :paramtype asset_id: str + :keyword data_container_name: + :paramtype data_container_name: str + :keyword version_id: + :paramtype version_id: str + """ + super(DataCallRequest, self).__init__(**kwargs) + self.data_uri = data_uri + self.data_type = data_type + self.asset_id = asset_id + self.data_container_name = data_container_name + self.version_id = version_id + + +class DataContainer(msrest.serialization.Model): + """DataContainer. + + All required parameters must be populated in order to send to Azure. + + :ivar name: Required. + :vartype name: str + :ivar data_type: Required. + :vartype data_type: str + :ivar mutable_props: + :vartype mutable_props: ~azure.mgmt.machinelearningservices.models.DataContainerMutable + :ivar is_registered: + :vartype is_registered: bool + """ + + _validation = { + 'name': {'required': True}, + 'data_type': {'required': True}, + } + + _attribute_map = { + 'name': {'key': 'name', 'type': 'str'}, + 'data_type': {'key': 'dataType', 'type': 'str'}, + 'mutable_props': {'key': 'mutableProps', 'type': 'DataContainerMutable'}, + 'is_registered': {'key': 'isRegistered', 'type': 'bool'}, + } + + def __init__( + self, + *, + name: str, + data_type: str, + mutable_props: Optional["DataContainerMutable"] = None, + is_registered: Optional[bool] = None, + **kwargs + ): + """ + :keyword name: Required. + :paramtype name: str + :keyword data_type: Required. + :paramtype data_type: str + :keyword mutable_props: + :paramtype mutable_props: ~azure.mgmt.machinelearningservices.models.DataContainerMutable + :keyword is_registered: + :paramtype is_registered: bool + """ + super(DataContainer, self).__init__(**kwargs) + self.name = name + self.data_type = data_type + self.mutable_props = mutable_props + self.is_registered = is_registered + + +class DataContainerEntity(msrest.serialization.Model): + """DataContainerEntity. + + :ivar data_container: + :vartype data_container: ~azure.mgmt.machinelearningservices.models.DataContainer + :ivar entity_metadata: + :vartype entity_metadata: ~azure.mgmt.machinelearningservices.models.EntityMetadata + :ivar latest_version: + :vartype latest_version: ~azure.mgmt.machinelearningservices.models.DataVersionEntity + :ivar next_version_id: + :vartype next_version_id: str + :ivar legacy_dataset_type: + :vartype legacy_dataset_type: str + """ + + _attribute_map = { + 'data_container': {'key': 'dataContainer', 'type': 'DataContainer'}, + 'entity_metadata': {'key': 'entityMetadata', 'type': 'EntityMetadata'}, + 'latest_version': {'key': 'latestVersion', 'type': 'DataVersionEntity'}, + 'next_version_id': {'key': 'nextVersionId', 'type': 'str'}, + 'legacy_dataset_type': {'key': 'legacyDatasetType', 'type': 'str'}, + } + + def __init__( + self, + *, + data_container: Optional["DataContainer"] = None, + entity_metadata: Optional["EntityMetadata"] = None, + latest_version: Optional["DataVersionEntity"] = None, + next_version_id: Optional[str] = None, + legacy_dataset_type: Optional[str] = None, + **kwargs + ): + """ + :keyword data_container: + :paramtype data_container: ~azure.mgmt.machinelearningservices.models.DataContainer + :keyword entity_metadata: + :paramtype entity_metadata: ~azure.mgmt.machinelearningservices.models.EntityMetadata + :keyword latest_version: + :paramtype latest_version: ~azure.mgmt.machinelearningservices.models.DataVersionEntity + :keyword next_version_id: + :paramtype next_version_id: str + :keyword legacy_dataset_type: + :paramtype legacy_dataset_type: str + """ + super(DataContainerEntity, self).__init__(**kwargs) + self.data_container = data_container + self.entity_metadata = entity_metadata + self.latest_version = latest_version + self.next_version_id = next_version_id + self.legacy_dataset_type = legacy_dataset_type + + +class DataContainerMutable(msrest.serialization.Model): + """DataContainerMutable. + + :ivar description: + :vartype description: str + :ivar tags: A set of tags. Dictionary of :code:`<string>`. + :vartype tags: dict[str, str] + :ivar is_archived: + :vartype is_archived: bool + """ + + _attribute_map = { + 'description': {'key': 'description', 'type': 'str'}, + 'tags': {'key': 'tags', 'type': '{str}'}, + 'is_archived': {'key': 'isArchived', 'type': 'bool'}, + } + + def __init__( + self, + *, + description: Optional[str] = None, + tags: Optional[Dict[str, str]] = None, + is_archived: Optional[bool] = None, + **kwargs + ): + """ + :keyword description: + :paramtype description: str + :keyword tags: A set of tags. Dictionary of :code:`<string>`. + :paramtype tags: dict[str, str] + :keyword is_archived: + :paramtype is_archived: bool + """ + super(DataContainerMutable, self).__init__(**kwargs) + self.description = description + self.tags = tags + self.is_archived = is_archived + + +class DataField(msrest.serialization.Model): + """DataField. + + :ivar type: Possible values include: "String", "Boolean", "Integer", "Decimal", "Date", + "Unknown", "Error", "Null", "DataRow", "List", "Stream". + :vartype type: str or ~azure.mgmt.machinelearningservices.models.FieldType + :ivar value: Anything. + :vartype value: any + """ + + _attribute_map = { + 'type': {'key': 'type', 'type': 'str'}, + 'value': {'key': 'value', 'type': 'object'}, + } + + def __init__( + self, + *, + type: Optional[Union[str, "FieldType"]] = None, + value: Optional[Any] = None, + **kwargs + ): + """ + :keyword type: Possible values include: "String", "Boolean", "Integer", "Decimal", "Date", + "Unknown", "Error", "Null", "DataRow", "List", "Stream". + :paramtype type: str or ~azure.mgmt.machinelearningservices.models.FieldType + :keyword value: Anything. + :paramtype value: any + """ + super(DataField, self).__init__(**kwargs) + self.type = type + self.value = value + + +class Dataset(msrest.serialization.Model): + """Dataset. + + :ivar dataset_id: + :vartype dataset_id: str + :ivar dataset_state: + :vartype dataset_state: ~azure.mgmt.machinelearningservices.models.DatasetState + :ivar latest: + :vartype latest: ~azure.mgmt.machinelearningservices.models.DatasetDefinition + :ivar next_version_id: + :vartype next_version_id: str + :ivar created_time: + :vartype created_time: ~datetime.datetime + :ivar modified_time: + :vartype modified_time: ~datetime.datetime + :ivar etag: + :vartype etag: str + :ivar name: + :vartype name: str + :ivar description: + :vartype description: str + :ivar tags: A set of tags. Dictionary of :code:`<string>`. + :vartype tags: dict[str, str] + :ivar is_visible: + :vartype is_visible: bool + :ivar default_compute: + :vartype default_compute: str + :ivar dataset_type: + :vartype dataset_type: str + """ + + _attribute_map = { + 'dataset_id': {'key': 'datasetId', 'type': 'str'}, + 'dataset_state': {'key': 'datasetState', 'type': 'DatasetState'}, + 'latest': {'key': 'latest', 'type': 'DatasetDefinition'}, + 'next_version_id': {'key': 'nextVersionId', 'type': 'str'}, + 'created_time': {'key': 'createdTime', 'type': 'iso-8601'}, + 'modified_time': {'key': 'modifiedTime', 'type': 'iso-8601'}, + 'etag': {'key': 'etag', 'type': 'str'}, + 'name': {'key': 'name', 'type': 'str'}, + 'description': {'key': 'description', 'type': 'str'}, + 'tags': {'key': 'tags', 'type': '{str}'}, + 'is_visible': {'key': 'isVisible', 'type': 'bool'}, + 'default_compute': {'key': 'defaultCompute', 'type': 'str'}, + 'dataset_type': {'key': 'datasetType', 'type': 'str'}, + } + + def __init__( + self, + *, + dataset_id: Optional[str] = None, + dataset_state: Optional["DatasetState"] = None, + latest: Optional["DatasetDefinition"] = None, + next_version_id: Optional[str] = None, + created_time: Optional[datetime.datetime] = None, + modified_time: Optional[datetime.datetime] = None, + etag: Optional[str] = None, + name: Optional[str] = None, + description: Optional[str] = None, + tags: Optional[Dict[str, str]] = None, + is_visible: Optional[bool] = None, + default_compute: Optional[str] = None, + dataset_type: Optional[str] = None, + **kwargs + ): + """ + :keyword dataset_id: + :paramtype dataset_id: str + :keyword dataset_state: + :paramtype dataset_state: ~azure.mgmt.machinelearningservices.models.DatasetState + :keyword latest: + :paramtype latest: ~azure.mgmt.machinelearningservices.models.DatasetDefinition + :keyword next_version_id: + :paramtype next_version_id: str + :keyword created_time: + :paramtype created_time: ~datetime.datetime + :keyword modified_time: + :paramtype modified_time: ~datetime.datetime + :keyword etag: + :paramtype etag: str + :keyword name: + :paramtype name: str + :keyword description: + :paramtype description: str + :keyword tags: A set of tags. Dictionary of :code:`<string>`. + :paramtype tags: dict[str, str] + :keyword is_visible: + :paramtype is_visible: bool + :keyword default_compute: + :paramtype default_compute: str + :keyword dataset_type: + :paramtype dataset_type: str + """ + super(Dataset, self).__init__(**kwargs) + self.dataset_id = dataset_id + self.dataset_state = dataset_state + self.latest = latest + self.next_version_id = next_version_id + self.created_time = created_time + self.modified_time = modified_time + self.etag = etag + self.name = name + self.description = description + self.tags = tags + self.is_visible = is_visible + self.default_compute = default_compute + self.dataset_type = dataset_type + + +class DatasetDefinition(msrest.serialization.Model): + """DatasetDefinition. + + :ivar dataset_id: + :vartype dataset_id: str + :ivar version_id: + :vartype version_id: str + :ivar dataset_definition_state: + :vartype dataset_definition_state: ~azure.mgmt.machinelearningservices.models.DatasetState + :ivar dataflow: + :vartype dataflow: str + :ivar dataflow_type: Possible values include: "Json", "Yaml". + :vartype dataflow_type: str or ~azure.mgmt.machinelearningservices.models.DataflowType + :ivar data_path: + :vartype data_path: ~azure.mgmt.machinelearningservices.models.DatasetPath + :ivar partition_format_in_path: + :vartype partition_format_in_path: str + :ivar profile_action_result: + :vartype profile_action_result: ~azure.mgmt.machinelearningservices.models.ProfileActionResult + :ivar notes: + :vartype notes: str + :ivar etag: + :vartype etag: str + :ivar created_time: + :vartype created_time: ~datetime.datetime + :ivar modified_time: + :vartype modified_time: ~datetime.datetime + :ivar data_expiry_time: + :vartype data_expiry_time: ~datetime.datetime + :ivar created_by: + :vartype created_by: ~azure.mgmt.machinelearningservices.models.User + :ivar modified_by: + :vartype modified_by: ~azure.mgmt.machinelearningservices.models.User + :ivar file_type: + :vartype file_type: str + :ivar properties: Dictionary of :code:`<any>`. + :vartype properties: dict[str, any] + :ivar saved_dataset_id: + :vartype saved_dataset_id: str + :ivar telemetry_info: Dictionary of :code:`<string>`. + :vartype telemetry_info: dict[str, str] + :ivar use_description_tags_from_definition: + :vartype use_description_tags_from_definition: bool + :ivar description: + :vartype description: str + :ivar tags: A set of tags. Dictionary of :code:`<string>`. + :vartype tags: dict[str, str] + """ + + _attribute_map = { + 'dataset_id': {'key': 'datasetId', 'type': 'str'}, + 'version_id': {'key': 'versionId', 'type': 'str'}, + 'dataset_definition_state': {'key': 'datasetDefinitionState', 'type': 'DatasetState'}, + 'dataflow': {'key': 'dataflow', 'type': 'str'}, + 'dataflow_type': {'key': 'dataflowType', 'type': 'str'}, + 'data_path': {'key': 'dataPath', 'type': 'DatasetPath'}, + 'partition_format_in_path': {'key': 'partitionFormatInPath', 'type': 'str'}, + 'profile_action_result': {'key': 'profileActionResult', 'type': 'ProfileActionResult'}, + 'notes': {'key': 'notes', 'type': 'str'}, + 'etag': {'key': 'etag', 'type': 'str'}, + 'created_time': {'key': 'createdTime', 'type': 'iso-8601'}, + 'modified_time': {'key': 'modifiedTime', 'type': 'iso-8601'}, + 'data_expiry_time': {'key': 'dataExpiryTime', 'type': 'iso-8601'}, + 'created_by': {'key': 'createdBy', 'type': 'User'}, + 'modified_by': {'key': 'modifiedBy', 'type': 'User'}, + 'file_type': {'key': 'fileType', 'type': 'str'}, + 'properties': {'key': 'properties', 'type': '{object}'}, + 'saved_dataset_id': {'key': 'savedDatasetId', 'type': 'str'}, + 'telemetry_info': {'key': 'telemetryInfo', 'type': '{str}'}, + 'use_description_tags_from_definition': {'key': 'useDescriptionTagsFromDefinition', 'type': 'bool'}, + 'description': {'key': 'description', 'type': 'str'}, + 'tags': {'key': 'tags', 'type': '{str}'}, + } + + def __init__( + self, + *, + dataset_id: Optional[str] = None, + version_id: Optional[str] = None, + dataset_definition_state: Optional["DatasetState"] = None, + dataflow: Optional[str] = None, + dataflow_type: Optional[Union[str, "DataflowType"]] = None, + data_path: Optional["DatasetPath"] = None, + partition_format_in_path: Optional[str] = None, + profile_action_result: Optional["ProfileActionResult"] = None, + notes: Optional[str] = None, + etag: Optional[str] = None, + created_time: Optional[datetime.datetime] = None, + modified_time: Optional[datetime.datetime] = None, + data_expiry_time: Optional[datetime.datetime] = None, + created_by: Optional["User"] = None, + modified_by: Optional["User"] = None, + file_type: Optional[str] = None, + properties: Optional[Dict[str, Any]] = None, + saved_dataset_id: Optional[str] = None, + telemetry_info: Optional[Dict[str, str]] = None, + use_description_tags_from_definition: Optional[bool] = None, + description: Optional[str] = None, + tags: Optional[Dict[str, str]] = None, + **kwargs + ): + """ + :keyword dataset_id: + :paramtype dataset_id: str + :keyword version_id: + :paramtype version_id: str + :keyword dataset_definition_state: + :paramtype dataset_definition_state: ~azure.mgmt.machinelearningservices.models.DatasetState + :keyword dataflow: + :paramtype dataflow: str + :keyword dataflow_type: Possible values include: "Json", "Yaml". + :paramtype dataflow_type: str or ~azure.mgmt.machinelearningservices.models.DataflowType + :keyword data_path: + :paramtype data_path: ~azure.mgmt.machinelearningservices.models.DatasetPath + :keyword partition_format_in_path: + :paramtype partition_format_in_path: str + :keyword profile_action_result: + :paramtype profile_action_result: + ~azure.mgmt.machinelearningservices.models.ProfileActionResult + :keyword notes: + :paramtype notes: str + :keyword etag: + :paramtype etag: str + :keyword created_time: + :paramtype created_time: ~datetime.datetime + :keyword modified_time: + :paramtype modified_time: ~datetime.datetime + :keyword data_expiry_time: + :paramtype data_expiry_time: ~datetime.datetime + :keyword created_by: + :paramtype created_by: ~azure.mgmt.machinelearningservices.models.User + :keyword modified_by: + :paramtype modified_by: ~azure.mgmt.machinelearningservices.models.User + :keyword file_type: + :paramtype file_type: str + :keyword properties: Dictionary of :code:`<any>`. + :paramtype properties: dict[str, any] + :keyword saved_dataset_id: + :paramtype saved_dataset_id: str + :keyword telemetry_info: Dictionary of :code:`<string>`. + :paramtype telemetry_info: dict[str, str] + :keyword use_description_tags_from_definition: + :paramtype use_description_tags_from_definition: bool + :keyword description: + :paramtype description: str + :keyword tags: A set of tags. Dictionary of :code:`<string>`. + :paramtype tags: dict[str, str] + """ + super(DatasetDefinition, self).__init__(**kwargs) + self.dataset_id = dataset_id + self.version_id = version_id + self.dataset_definition_state = dataset_definition_state + self.dataflow = dataflow + self.dataflow_type = dataflow_type + self.data_path = data_path + self.partition_format_in_path = partition_format_in_path + self.profile_action_result = profile_action_result + self.notes = notes + self.etag = etag + self.created_time = created_time + self.modified_time = modified_time + self.data_expiry_time = data_expiry_time + self.created_by = created_by + self.modified_by = modified_by + self.file_type = file_type + self.properties = properties + self.saved_dataset_id = saved_dataset_id + self.telemetry_info = telemetry_info + self.use_description_tags_from_definition = use_description_tags_from_definition + self.description = description + self.tags = tags + + +class DatasetDefinitionReference(msrest.serialization.Model): + """DatasetDefinitionReference. + + :ivar dataset_id: + :vartype dataset_id: str + :ivar definition_version: + :vartype definition_version: str + """ + + _attribute_map = { + 'dataset_id': {'key': 'datasetId', 'type': 'str'}, + 'definition_version': {'key': 'definitionVersion', 'type': 'str'}, + } + + def __init__( + self, + *, + dataset_id: Optional[str] = None, + definition_version: Optional[str] = None, + **kwargs + ): + """ + :keyword dataset_id: + :paramtype dataset_id: str + :keyword definition_version: + :paramtype definition_version: str + """ + super(DatasetDefinitionReference, self).__init__(**kwargs) + self.dataset_id = dataset_id + self.definition_version = definition_version + + +class DatasetPath(msrest.serialization.Model): + """DatasetPath. + + :ivar datastore_name: + :vartype datastore_name: str + :ivar relative_path: + :vartype relative_path: str + :ivar azure_file_path: + :vartype azure_file_path: str + :ivar paths: + :vartype paths: list[str] + :ivar sql_data_path: + :vartype sql_data_path: ~azure.mgmt.machinelearningservices.models.SqlDataPath + :ivar http_url: + :vartype http_url: str + :ivar additional_properties: Dictionary of :code:`<any>`. + :vartype additional_properties: dict[str, any] + :ivar partition_format: + :vartype partition_format: str + :ivar partition_format_ignore_error: + :vartype partition_format_ignore_error: bool + """ + + _attribute_map = { + 'datastore_name': {'key': 'datastoreName', 'type': 'str'}, + 'relative_path': {'key': 'relativePath', 'type': 'str'}, + 'azure_file_path': {'key': 'azureFilePath', 'type': 'str'}, + 'paths': {'key': 'paths', 'type': '[str]'}, + 'sql_data_path': {'key': 'sqlDataPath', 'type': 'SqlDataPath'}, + 'http_url': {'key': 'httpUrl', 'type': 'str'}, + 'additional_properties': {'key': 'additionalProperties', 'type': '{object}'}, + 'partition_format': {'key': 'partitionFormat', 'type': 'str'}, + 'partition_format_ignore_error': {'key': 'partitionFormatIgnoreError', 'type': 'bool'}, + } + + def __init__( + self, + *, + datastore_name: Optional[str] = None, + relative_path: Optional[str] = None, + azure_file_path: Optional[str] = None, + paths: Optional[List[str]] = None, + sql_data_path: Optional["SqlDataPath"] = None, + http_url: Optional[str] = None, + additional_properties: Optional[Dict[str, Any]] = None, + partition_format: Optional[str] = None, + partition_format_ignore_error: Optional[bool] = None, + **kwargs + ): + """ + :keyword datastore_name: + :paramtype datastore_name: str + :keyword relative_path: + :paramtype relative_path: str + :keyword azure_file_path: + :paramtype azure_file_path: str + :keyword paths: + :paramtype paths: list[str] + :keyword sql_data_path: + :paramtype sql_data_path: ~azure.mgmt.machinelearningservices.models.SqlDataPath + :keyword http_url: + :paramtype http_url: str + :keyword additional_properties: Dictionary of :code:`<any>`. + :paramtype additional_properties: dict[str, any] + :keyword partition_format: + :paramtype partition_format: str + :keyword partition_format_ignore_error: + :paramtype partition_format_ignore_error: bool + """ + super(DatasetPath, self).__init__(**kwargs) + self.datastore_name = datastore_name + self.relative_path = relative_path + self.azure_file_path = azure_file_path + self.paths = paths + self.sql_data_path = sql_data_path + self.http_url = http_url + self.additional_properties = additional_properties + self.partition_format = partition_format + self.partition_format_ignore_error = partition_format_ignore_error + + +class DatasetState(msrest.serialization.Model): + """DatasetState. + + :ivar state: + :vartype state: str + :ivar deprecated_by: + :vartype deprecated_by: ~azure.mgmt.machinelearningservices.models.DatasetDefinitionReference + :ivar etag: + :vartype etag: str + """ + + _attribute_map = { + 'state': {'key': 'state', 'type': 'str'}, + 'deprecated_by': {'key': 'deprecatedBy', 'type': 'DatasetDefinitionReference'}, + 'etag': {'key': 'etag', 'type': 'str'}, + } + + def __init__( + self, + *, + state: Optional[str] = None, + deprecated_by: Optional["DatasetDefinitionReference"] = None, + etag: Optional[str] = None, + **kwargs + ): + """ + :keyword state: + :paramtype state: str + :keyword deprecated_by: + :paramtype deprecated_by: ~azure.mgmt.machinelearningservices.models.DatasetDefinitionReference + :keyword etag: + :paramtype etag: str + """ + super(DatasetState, self).__init__(**kwargs) + self.state = state + self.deprecated_by = deprecated_by + self.etag = etag + + +class DatasetV2(msrest.serialization.Model): + """DatasetV2. + + :ivar dataset_id: + :vartype dataset_id: str + :ivar name: + :vartype name: str + :ivar version_id: + :vartype version_id: str + :ivar dataflow: + :vartype dataflow: str + :ivar created_time: + :vartype created_time: ~datetime.datetime + :ivar modified_time: + :vartype modified_time: ~datetime.datetime + :ivar created_by: + :vartype created_by: ~azure.mgmt.machinelearningservices.models.User + :ivar modified_by: + :vartype modified_by: ~azure.mgmt.machinelearningservices.models.User + :ivar properties: Dictionary of :code:`<string>`. + :vartype properties: dict[str, str] + :ivar telemetry_info: Dictionary of :code:`<string>`. + :vartype telemetry_info: dict[str, str] + :ivar description: + :vartype description: str + :ivar is_anonymous: + :vartype is_anonymous: bool + :ivar tags: A set of tags. Dictionary of :code:`<string>`. + :vartype tags: dict[str, str] + :ivar legacy_properties: Dictionary of :code:`<any>`. + :vartype legacy_properties: dict[str, any] + :ivar data_expiry_time: + :vartype data_expiry_time: ~datetime.datetime + :ivar legacy: Dictionary of :code:`<any>`. + :vartype legacy: dict[str, any] + """ + + _attribute_map = { + 'dataset_id': {'key': 'datasetId', 'type': 'str'}, + 'name': {'key': 'name', 'type': 'str'}, + 'version_id': {'key': 'versionId', 'type': 'str'}, + 'dataflow': {'key': 'dataflow', 'type': 'str'}, + 'created_time': {'key': 'createdTime', 'type': 'iso-8601'}, + 'modified_time': {'key': 'modifiedTime', 'type': 'iso-8601'}, + 'created_by': {'key': 'createdBy', 'type': 'User'}, + 'modified_by': {'key': 'modifiedBy', 'type': 'User'}, + 'properties': {'key': 'properties', 'type': '{str}'}, + 'telemetry_info': {'key': 'telemetryInfo', 'type': '{str}'}, + 'description': {'key': 'description', 'type': 'str'}, + 'is_anonymous': {'key': 'isAnonymous', 'type': 'bool'}, + 'tags': {'key': 'tags', 'type': '{str}'}, + 'legacy_properties': {'key': 'legacyProperties', 'type': '{object}'}, + 'data_expiry_time': {'key': 'dataExpiryTime', 'type': 'iso-8601'}, + 'legacy': {'key': 'legacy', 'type': '{object}'}, + } + + def __init__( + self, + *, + dataset_id: Optional[str] = None, + name: Optional[str] = None, + version_id: Optional[str] = None, + dataflow: Optional[str] = None, + created_time: Optional[datetime.datetime] = None, + modified_time: Optional[datetime.datetime] = None, + created_by: Optional["User"] = None, + modified_by: Optional["User"] = None, + properties: Optional[Dict[str, str]] = None, + telemetry_info: Optional[Dict[str, str]] = None, + description: Optional[str] = None, + is_anonymous: Optional[bool] = None, + tags: Optional[Dict[str, str]] = None, + legacy_properties: Optional[Dict[str, Any]] = None, + data_expiry_time: Optional[datetime.datetime] = None, + legacy: Optional[Dict[str, Any]] = None, + **kwargs + ): + """ + :keyword dataset_id: + :paramtype dataset_id: str + :keyword name: + :paramtype name: str + :keyword version_id: + :paramtype version_id: str + :keyword dataflow: + :paramtype dataflow: str + :keyword created_time: + :paramtype created_time: ~datetime.datetime + :keyword modified_time: + :paramtype modified_time: ~datetime.datetime + :keyword created_by: + :paramtype created_by: ~azure.mgmt.machinelearningservices.models.User + :keyword modified_by: + :paramtype modified_by: ~azure.mgmt.machinelearningservices.models.User + :keyword properties: Dictionary of :code:`<string>`. + :paramtype properties: dict[str, str] + :keyword telemetry_info: Dictionary of :code:`<string>`. + :paramtype telemetry_info: dict[str, str] + :keyword description: + :paramtype description: str + :keyword is_anonymous: + :paramtype is_anonymous: bool + :keyword tags: A set of tags. Dictionary of :code:`<string>`. + :paramtype tags: dict[str, str] + :keyword legacy_properties: Dictionary of :code:`<any>`. + :paramtype legacy_properties: dict[str, any] + :keyword data_expiry_time: + :paramtype data_expiry_time: ~datetime.datetime + :keyword legacy: Dictionary of :code:`<any>`. + :paramtype legacy: dict[str, any] + """ + super(DatasetV2, self).__init__(**kwargs) + self.dataset_id = dataset_id + self.name = name + self.version_id = version_id + self.dataflow = dataflow + self.created_time = created_time + self.modified_time = modified_time + self.created_by = created_by + self.modified_by = modified_by + self.properties = properties + self.telemetry_info = telemetry_info + self.description = description + self.is_anonymous = is_anonymous + self.tags = tags + self.legacy_properties = legacy_properties + self.data_expiry_time = data_expiry_time + self.legacy = legacy + + +class DataUriV2Response(msrest.serialization.Model): + """DataUriV2Response. + + :ivar uri: + :vartype uri: str + :ivar type: + :vartype type: str + """ + + _attribute_map = { + 'uri': {'key': 'uri', 'type': 'str'}, + 'type': {'key': 'type', 'type': 'str'}, + } + + def __init__( + self, + *, + uri: Optional[str] = None, + type: Optional[str] = None, + **kwargs + ): + """ + :keyword uri: + :paramtype uri: str + :keyword type: + :paramtype type: str + """ + super(DataUriV2Response, self).__init__(**kwargs) + self.uri = uri + self.type = type + + +class DataVersion(msrest.serialization.Model): + """DataVersion. + + All required parameters must be populated in order to send to Azure. + + :ivar asset_id: + :vartype asset_id: str + :ivar data_container_name: Required. + :vartype data_container_name: str + :ivar data_type: Required. + :vartype data_type: str + :ivar data_uri: Required. + :vartype data_uri: str + :ivar version_id: Required. + :vartype version_id: str + :ivar mutable_props: + :vartype mutable_props: ~azure.mgmt.machinelearningservices.models.DataVersionMutable + :ivar referenced_data_uris: + :vartype referenced_data_uris: list[str] + :ivar properties: Dictionary of :code:`<string>`. + :vartype properties: dict[str, str] + """ + + _validation = { + 'data_container_name': {'required': True}, + 'data_type': {'required': True}, + 'data_uri': {'required': True}, + 'version_id': {'required': True}, + } + + _attribute_map = { + 'asset_id': {'key': 'assetId', 'type': 'str'}, + 'data_container_name': {'key': 'dataContainerName', 'type': 'str'}, + 'data_type': {'key': 'dataType', 'type': 'str'}, + 'data_uri': {'key': 'dataUri', 'type': 'str'}, + 'version_id': {'key': 'versionId', 'type': 'str'}, + 'mutable_props': {'key': 'mutableProps', 'type': 'DataVersionMutable'}, + 'referenced_data_uris': {'key': 'referencedDataUris', 'type': '[str]'}, + 'properties': {'key': 'properties', 'type': '{str}'}, + } + + def __init__( + self, + *, + data_container_name: str, + data_type: str, + data_uri: str, + version_id: str, + asset_id: Optional[str] = None, + mutable_props: Optional["DataVersionMutable"] = None, + referenced_data_uris: Optional[List[str]] = None, + properties: Optional[Dict[str, str]] = None, + **kwargs + ): + """ + :keyword asset_id: + :paramtype asset_id: str + :keyword data_container_name: Required. + :paramtype data_container_name: str + :keyword data_type: Required. + :paramtype data_type: str + :keyword data_uri: Required. + :paramtype data_uri: str + :keyword version_id: Required. + :paramtype version_id: str + :keyword mutable_props: + :paramtype mutable_props: ~azure.mgmt.machinelearningservices.models.DataVersionMutable + :keyword referenced_data_uris: + :paramtype referenced_data_uris: list[str] + :keyword properties: Dictionary of :code:`<string>`. + :paramtype properties: dict[str, str] + """ + super(DataVersion, self).__init__(**kwargs) + self.asset_id = asset_id + self.data_container_name = data_container_name + self.data_type = data_type + self.data_uri = data_uri + self.version_id = version_id + self.mutable_props = mutable_props + self.referenced_data_uris = referenced_data_uris + self.properties = properties + + +class DataVersionEntity(msrest.serialization.Model): + """DataVersionEntity. + + :ivar data_version: + :vartype data_version: ~azure.mgmt.machinelearningservices.models.DataVersion + :ivar entity_metadata: + :vartype entity_metadata: ~azure.mgmt.machinelearningservices.models.EntityMetadata + """ + + _attribute_map = { + 'data_version': {'key': 'dataVersion', 'type': 'DataVersion'}, + 'entity_metadata': {'key': 'entityMetadata', 'type': 'EntityMetadata'}, + } + + def __init__( + self, + *, + data_version: Optional["DataVersion"] = None, + entity_metadata: Optional["EntityMetadata"] = None, + **kwargs + ): + """ + :keyword data_version: + :paramtype data_version: ~azure.mgmt.machinelearningservices.models.DataVersion + :keyword entity_metadata: + :paramtype entity_metadata: ~azure.mgmt.machinelearningservices.models.EntityMetadata + """ + super(DataVersionEntity, self).__init__(**kwargs) + self.data_version = data_version + self.entity_metadata = entity_metadata + + +class DataVersionMutable(msrest.serialization.Model): + """DataVersionMutable. + + :ivar data_expiry_time: + :vartype data_expiry_time: ~datetime.datetime + :ivar description: + :vartype description: str + :ivar tags: A set of tags. Dictionary of :code:`<string>`. + :vartype tags: dict[str, str] + :ivar is_archived: + :vartype is_archived: bool + """ + + _attribute_map = { + 'data_expiry_time': {'key': 'dataExpiryTime', 'type': 'iso-8601'}, + 'description': {'key': 'description', 'type': 'str'}, + 'tags': {'key': 'tags', 'type': '{str}'}, + 'is_archived': {'key': 'isArchived', 'type': 'bool'}, + } + + def __init__( + self, + *, + data_expiry_time: Optional[datetime.datetime] = None, + description: Optional[str] = None, + tags: Optional[Dict[str, str]] = None, + is_archived: Optional[bool] = None, + **kwargs + ): + """ + :keyword data_expiry_time: + :paramtype data_expiry_time: ~datetime.datetime + :keyword description: + :paramtype description: str + :keyword tags: A set of tags. Dictionary of :code:`<string>`. + :paramtype tags: dict[str, str] + :keyword is_archived: + :paramtype is_archived: bool + """ + super(DataVersionMutable, self).__init__(**kwargs) + self.data_expiry_time = data_expiry_time + self.description = description + self.tags = tags + self.is_archived = is_archived + + +class DataViewSetResult(msrest.serialization.Model): + """DataViewSetResult. + + :ivar schema: + :vartype schema: list[~azure.mgmt.machinelearningservices.models.ColumnDefinition] + :ivar rows: + :vartype rows: list[list[~azure.mgmt.machinelearningservices.models.DataField]] + """ + + _attribute_map = { + 'schema': {'key': 'schema', 'type': '[ColumnDefinition]'}, + 'rows': {'key': 'rows', 'type': '[[DataField]]'}, + } + + def __init__( + self, + *, + schema: Optional[List["ColumnDefinition"]] = None, + rows: Optional[List[List["DataField"]]] = None, + **kwargs + ): + """ + :keyword schema: + :paramtype schema: list[~azure.mgmt.machinelearningservices.models.ColumnDefinition] + :keyword rows: + :paramtype rows: list[list[~azure.mgmt.machinelearningservices.models.DataField]] + """ + super(DataViewSetResult, self).__init__(**kwargs) + self.schema = schema + self.rows = rows + + +class EntityMetadata(msrest.serialization.Model): + """EntityMetadata. + + :ivar etag: + :vartype etag: str + :ivar created_time: + :vartype created_time: ~datetime.datetime + :ivar modified_time: + :vartype modified_time: ~datetime.datetime + :ivar created_by: + :vartype created_by: ~azure.mgmt.machinelearningservices.models.User + :ivar modified_by: + :vartype modified_by: ~azure.mgmt.machinelearningservices.models.User + """ + + _attribute_map = { + 'etag': {'key': 'etag', 'type': 'str'}, + 'created_time': {'key': 'createdTime', 'type': 'iso-8601'}, + 'modified_time': {'key': 'modifiedTime', 'type': 'iso-8601'}, + 'created_by': {'key': 'createdBy', 'type': 'User'}, + 'modified_by': {'key': 'modifiedBy', 'type': 'User'}, + } + + def __init__( + self, + *, + etag: Optional[str] = None, + created_time: Optional[datetime.datetime] = None, + modified_time: Optional[datetime.datetime] = None, + created_by: Optional["User"] = None, + modified_by: Optional["User"] = None, + **kwargs + ): + """ + :keyword etag: + :paramtype etag: str + :keyword created_time: + :paramtype created_time: ~datetime.datetime + :keyword modified_time: + :paramtype modified_time: ~datetime.datetime + :keyword created_by: + :paramtype created_by: ~azure.mgmt.machinelearningservices.models.User + :keyword modified_by: + :paramtype modified_by: ~azure.mgmt.machinelearningservices.models.User + """ + super(EntityMetadata, self).__init__(**kwargs) + self.etag = etag + self.created_time = created_time + self.modified_time = modified_time + self.created_by = created_by + self.modified_by = modified_by + + +class ErrorAdditionalInfo(msrest.serialization.Model): + """The resource management error additional info. + + :ivar type: The additional info type. + :vartype type: str + :ivar info: The additional info. + :vartype info: any + """ + + _attribute_map = { + 'type': {'key': 'type', 'type': 'str'}, + 'info': {'key': 'info', 'type': 'object'}, + } + + def __init__( + self, + *, + type: Optional[str] = None, + info: Optional[Any] = None, + **kwargs + ): + """ + :keyword type: The additional info type. + :paramtype type: str + :keyword info: The additional info. + :paramtype info: any + """ + super(ErrorAdditionalInfo, self).__init__(**kwargs) + self.type = type + self.info = info + + +class ErrorResponse(msrest.serialization.Model): + """The error response. + + :ivar error: The root error. + :vartype error: ~azure.mgmt.machinelearningservices.models.RootError + :ivar correlation: Dictionary containing correlation details for the error. + :vartype correlation: dict[str, str] + :ivar environment: The hosting environment. + :vartype environment: str + :ivar location: The Azure region. + :vartype location: str + :ivar time: The time in UTC. + :vartype time: ~datetime.datetime + :ivar component_name: Component name where error originated/encountered. + :vartype component_name: str + """ + + _attribute_map = { + 'error': {'key': 'error', 'type': 'RootError'}, + 'correlation': {'key': 'correlation', 'type': '{str}'}, + 'environment': {'key': 'environment', 'type': 'str'}, + 'location': {'key': 'location', 'type': 'str'}, + 'time': {'key': 'time', 'type': 'iso-8601'}, + 'component_name': {'key': 'componentName', 'type': 'str'}, + } + + def __init__( + self, + *, + error: Optional["RootError"] = None, + correlation: Optional[Dict[str, str]] = None, + environment: Optional[str] = None, + location: Optional[str] = None, + time: Optional[datetime.datetime] = None, + component_name: Optional[str] = None, + **kwargs + ): + """ + :keyword error: The root error. + :paramtype error: ~azure.mgmt.machinelearningservices.models.RootError + :keyword correlation: Dictionary containing correlation details for the error. + :paramtype correlation: dict[str, str] + :keyword environment: The hosting environment. + :paramtype environment: str + :keyword location: The Azure region. + :paramtype location: str + :keyword time: The time in UTC. + :paramtype time: ~datetime.datetime + :keyword component_name: Component name where error originated/encountered. + :paramtype component_name: str + """ + super(ErrorResponse, self).__init__(**kwargs) + self.error = error + self.correlation = correlation + self.environment = environment + self.location = location + self.time = time + self.component_name = component_name + + +class HistogramBin(msrest.serialization.Model): + """HistogramBin. + + :ivar lower_bound: + :vartype lower_bound: float + :ivar upper_bound: + :vartype upper_bound: float + :ivar count: + :vartype count: float + """ + + _attribute_map = { + 'lower_bound': {'key': 'lowerBound', 'type': 'float'}, + 'upper_bound': {'key': 'upperBound', 'type': 'float'}, + 'count': {'key': 'count', 'type': 'float'}, + } + + def __init__( + self, + *, + lower_bound: Optional[float] = None, + upper_bound: Optional[float] = None, + count: Optional[float] = None, + **kwargs + ): + """ + :keyword lower_bound: + :paramtype lower_bound: float + :keyword upper_bound: + :paramtype upper_bound: float + :keyword count: + :paramtype count: float + """ + super(HistogramBin, self).__init__(**kwargs) + self.lower_bound = lower_bound + self.upper_bound = upper_bound + self.count = count + + +class HttpContent(msrest.serialization.Model): + """HttpContent. + + Variables are only populated by the server, and will be ignored when sending a request. + + :ivar headers: + :vartype headers: + list[~azure.mgmt.machinelearningservices.models.KeyValuePairStringIEnumerable1] + """ + + _validation = { + 'headers': {'readonly': True}, + } + + _attribute_map = { + 'headers': {'key': 'headers', 'type': '[KeyValuePairStringIEnumerable1]'}, + } + + def __init__( + self, + **kwargs + ): + """ + """ + super(HttpContent, self).__init__(**kwargs) + self.headers = None + + +class HttpMethod(msrest.serialization.Model): + """HttpMethod. + + :ivar method: + :vartype method: str + """ + + _attribute_map = { + 'method': {'key': 'method', 'type': 'str'}, + } + + def __init__( + self, + *, + method: Optional[str] = None, + **kwargs + ): + """ + :keyword method: + :paramtype method: str + """ + super(HttpMethod, self).__init__(**kwargs) + self.method = method + + +class HttpRequestMessage(msrest.serialization.Model): + """HttpRequestMessage. + + Variables are only populated by the server, and will be ignored when sending a request. + + :ivar version: + :vartype version: str + :ivar version_policy: Possible values include: "RequestVersionOrLower", + "RequestVersionOrHigher", "RequestVersionExact". + :vartype version_policy: str or ~azure.mgmt.machinelearningservices.models.HttpVersionPolicy + :ivar content: + :vartype content: ~azure.mgmt.machinelearningservices.models.HttpContent + :ivar method: + :vartype method: ~azure.mgmt.machinelearningservices.models.HttpMethod + :ivar request_uri: + :vartype request_uri: str + :ivar headers: + :vartype headers: + list[~azure.mgmt.machinelearningservices.models.KeyValuePairStringIEnumerable1] + :ivar options: Dictionary of :code:`<any>`. + :vartype options: dict[str, any] + """ + + _validation = { + 'headers': {'readonly': True}, + 'options': {'readonly': True}, + } + + _attribute_map = { + 'version': {'key': 'version', 'type': 'str'}, + 'version_policy': {'key': 'versionPolicy', 'type': 'str'}, + 'content': {'key': 'content', 'type': 'HttpContent'}, + 'method': {'key': 'method', 'type': 'HttpMethod'}, + 'request_uri': {'key': 'requestUri', 'type': 'str'}, + 'headers': {'key': 'headers', 'type': '[KeyValuePairStringIEnumerable1]'}, + 'options': {'key': 'options', 'type': '{object}'}, + } + + def __init__( + self, + *, + version: Optional[str] = None, + version_policy: Optional[Union[str, "HttpVersionPolicy"]] = None, + content: Optional["HttpContent"] = None, + method: Optional["HttpMethod"] = None, + request_uri: Optional[str] = None, + **kwargs + ): + """ + :keyword version: + :paramtype version: str + :keyword version_policy: Possible values include: "RequestVersionOrLower", + "RequestVersionOrHigher", "RequestVersionExact". + :paramtype version_policy: str or ~azure.mgmt.machinelearningservices.models.HttpVersionPolicy + :keyword content: + :paramtype content: ~azure.mgmt.machinelearningservices.models.HttpContent + :keyword method: + :paramtype method: ~azure.mgmt.machinelearningservices.models.HttpMethod + :keyword request_uri: + :paramtype request_uri: str + """ + super(HttpRequestMessage, self).__init__(**kwargs) + self.version = version + self.version_policy = version_policy + self.content = content + self.method = method + self.request_uri = request_uri + self.headers = None + self.options = None + + +class HttpResponseMessage(msrest.serialization.Model): + """HttpResponseMessage. + + Variables are only populated by the server, and will be ignored when sending a request. + + :ivar version: + :vartype version: str + :ivar content: + :vartype content: ~azure.mgmt.machinelearningservices.models.HttpContent + :ivar status_code: Possible values include: "Continue", "SwitchingProtocols", "Processing", + "EarlyHints", "OK", "Created", "Accepted", "NonAuthoritativeInformation", "NoContent", + "ResetContent", "PartialContent", "MultiStatus", "AlreadyReported", "IMUsed", "Ambiguous", + "Moved", "Redirect", "RedirectMethod", "NotModified", "UseProxy", "Unused", + "TemporaryRedirect", "PermanentRedirect", "BadRequest", "Unauthorized", "PaymentRequired", + "Forbidden", "NotFound", "MethodNotAllowed", "NotAcceptable", "ProxyAuthenticationRequired", + "RequestTimeout", "Conflict", "Gone", "LengthRequired", "PreconditionFailed", + "RequestEntityTooLarge", "RequestUriTooLong", "UnsupportedMediaType", + "RequestedRangeNotSatisfiable", "ExpectationFailed", "MisdirectedRequest", + "UnprocessableEntity", "Locked", "FailedDependency", "UpgradeRequired", "PreconditionRequired", + "TooManyRequests", "RequestHeaderFieldsTooLarge", "UnavailableForLegalReasons", + "InternalServerError", "NotImplemented", "BadGateway", "ServiceUnavailable", "GatewayTimeout", + "HttpVersionNotSupported", "VariantAlsoNegotiates", "InsufficientStorage", "LoopDetected", + "NotExtended", "NetworkAuthenticationRequired". + :vartype status_code: str or ~azure.mgmt.machinelearningservices.models.HttpStatusCode + :ivar reason_phrase: + :vartype reason_phrase: str + :ivar headers: + :vartype headers: + list[~azure.mgmt.machinelearningservices.models.KeyValuePairStringIEnumerable1] + :ivar trailing_headers: + :vartype trailing_headers: + list[~azure.mgmt.machinelearningservices.models.KeyValuePairStringIEnumerable1] + :ivar request_message: + :vartype request_message: ~azure.mgmt.machinelearningservices.models.HttpRequestMessage + :ivar is_success_status_code: + :vartype is_success_status_code: bool + """ + + _validation = { + 'headers': {'readonly': True}, + 'trailing_headers': {'readonly': True}, + 'is_success_status_code': {'readonly': True}, + } + + _attribute_map = { + 'version': {'key': 'version', 'type': 'str'}, + 'content': {'key': 'content', 'type': 'HttpContent'}, + 'status_code': {'key': 'statusCode', 'type': 'str'}, + 'reason_phrase': {'key': 'reasonPhrase', 'type': 'str'}, + 'headers': {'key': 'headers', 'type': '[KeyValuePairStringIEnumerable1]'}, + 'trailing_headers': {'key': 'trailingHeaders', 'type': '[KeyValuePairStringIEnumerable1]'}, + 'request_message': {'key': 'requestMessage', 'type': 'HttpRequestMessage'}, + 'is_success_status_code': {'key': 'isSuccessStatusCode', 'type': 'bool'}, + } + + def __init__( + self, + *, + version: Optional[str] = None, + content: Optional["HttpContent"] = None, + status_code: Optional[Union[str, "HttpStatusCode"]] = None, + reason_phrase: Optional[str] = None, + request_message: Optional["HttpRequestMessage"] = None, + **kwargs + ): + """ + :keyword version: + :paramtype version: str + :keyword content: + :paramtype content: ~azure.mgmt.machinelearningservices.models.HttpContent + :keyword status_code: Possible values include: "Continue", "SwitchingProtocols", "Processing", + "EarlyHints", "OK", "Created", "Accepted", "NonAuthoritativeInformation", "NoContent", + "ResetContent", "PartialContent", "MultiStatus", "AlreadyReported", "IMUsed", "Ambiguous", + "Moved", "Redirect", "RedirectMethod", "NotModified", "UseProxy", "Unused", + "TemporaryRedirect", "PermanentRedirect", "BadRequest", "Unauthorized", "PaymentRequired", + "Forbidden", "NotFound", "MethodNotAllowed", "NotAcceptable", "ProxyAuthenticationRequired", + "RequestTimeout", "Conflict", "Gone", "LengthRequired", "PreconditionFailed", + "RequestEntityTooLarge", "RequestUriTooLong", "UnsupportedMediaType", + "RequestedRangeNotSatisfiable", "ExpectationFailed", "MisdirectedRequest", + "UnprocessableEntity", "Locked", "FailedDependency", "UpgradeRequired", "PreconditionRequired", + "TooManyRequests", "RequestHeaderFieldsTooLarge", "UnavailableForLegalReasons", + "InternalServerError", "NotImplemented", "BadGateway", "ServiceUnavailable", "GatewayTimeout", + "HttpVersionNotSupported", "VariantAlsoNegotiates", "InsufficientStorage", "LoopDetected", + "NotExtended", "NetworkAuthenticationRequired". + :paramtype status_code: str or ~azure.mgmt.machinelearningservices.models.HttpStatusCode + :keyword reason_phrase: + :paramtype reason_phrase: str + :keyword request_message: + :paramtype request_message: ~azure.mgmt.machinelearningservices.models.HttpRequestMessage + """ + super(HttpResponseMessage, self).__init__(**kwargs) + self.version = version + self.content = content + self.status_code = status_code + self.reason_phrase = reason_phrase + self.headers = None + self.trailing_headers = None + self.request_message = request_message + self.is_success_status_code = None + + +class InnerErrorResponse(msrest.serialization.Model): + """A nested structure of errors. + + :ivar code: The error code. + :vartype code: str + :ivar inner_error: A nested structure of errors. + :vartype inner_error: ~azure.mgmt.machinelearningservices.models.InnerErrorResponse + """ + + _attribute_map = { + 'code': {'key': 'code', 'type': 'str'}, + 'inner_error': {'key': 'innerError', 'type': 'InnerErrorResponse'}, + } + + def __init__( + self, + *, + code: Optional[str] = None, + inner_error: Optional["InnerErrorResponse"] = None, + **kwargs + ): + """ + :keyword code: The error code. + :paramtype code: str + :keyword inner_error: A nested structure of errors. + :paramtype inner_error: ~azure.mgmt.machinelearningservices.models.InnerErrorResponse + """ + super(InnerErrorResponse, self).__init__(**kwargs) + self.code = code + self.inner_error = inner_error + + +class KeyValuePairStringIEnumerable1(msrest.serialization.Model): + """KeyValuePairStringIEnumerable1. + + :ivar key: + :vartype key: str + :ivar value: + :vartype value: list[str] + """ + + _attribute_map = { + 'key': {'key': 'key', 'type': 'str'}, + 'value': {'key': 'value', 'type': '[str]'}, + } + + def __init__( + self, + *, + key: Optional[str] = None, + value: Optional[List[str]] = None, + **kwargs + ): + """ + :keyword key: + :paramtype key: str + :keyword value: + :paramtype value: list[str] + """ + super(KeyValuePairStringIEnumerable1, self).__init__(**kwargs) + self.key = key + self.value = value + + +class LongRunningOperationResponse1LongRunningOperationResponseObject(msrest.serialization.Model): + """LongRunningOperationResponse1LongRunningOperationResponseObject. + + :ivar completion_result: Anything. + :vartype completion_result: any + :ivar location: + :vartype location: str + :ivar operation_result: + :vartype operation_result: str + """ + + _attribute_map = { + 'completion_result': {'key': 'completionResult', 'type': 'object'}, + 'location': {'key': 'location', 'type': 'str'}, + 'operation_result': {'key': 'operationResult', 'type': 'str'}, + } + + def __init__( + self, + *, + completion_result: Optional[Any] = None, + location: Optional[str] = None, + operation_result: Optional[str] = None, + **kwargs + ): + """ + :keyword completion_result: Anything. + :paramtype completion_result: any + :keyword location: + :paramtype location: str + :keyword operation_result: + :paramtype operation_result: str + """ + super(LongRunningOperationResponse1LongRunningOperationResponseObject, self).__init__(**kwargs) + self.completion_result = completion_result + self.location = location + self.operation_result = operation_result + + +class Moments(msrest.serialization.Model): + """Moments. + + :ivar mean: + :vartype mean: float + :ivar standard_deviation: + :vartype standard_deviation: float + :ivar variance: + :vartype variance: float + :ivar skewness: + :vartype skewness: float + :ivar kurtosis: + :vartype kurtosis: float + """ + + _attribute_map = { + 'mean': {'key': 'mean', 'type': 'float'}, + 'standard_deviation': {'key': 'standardDeviation', 'type': 'float'}, + 'variance': {'key': 'variance', 'type': 'float'}, + 'skewness': {'key': 'skewness', 'type': 'float'}, + 'kurtosis': {'key': 'kurtosis', 'type': 'float'}, + } + + def __init__( + self, + *, + mean: Optional[float] = None, + standard_deviation: Optional[float] = None, + variance: Optional[float] = None, + skewness: Optional[float] = None, + kurtosis: Optional[float] = None, + **kwargs + ): + """ + :keyword mean: + :paramtype mean: float + :keyword standard_deviation: + :paramtype standard_deviation: float + :keyword variance: + :paramtype variance: float + :keyword skewness: + :paramtype skewness: float + :keyword kurtosis: + :paramtype kurtosis: float + """ + super(Moments, self).__init__(**kwargs) + self.mean = mean + self.standard_deviation = standard_deviation + self.variance = variance + self.skewness = skewness + self.kurtosis = kurtosis + + +class PaginatedDataContainerEntityList(msrest.serialization.Model): + """A paginated list of DataContainerEntitys. + + :ivar value: An array of objects of type DataContainerEntity. + :vartype value: list[~azure.mgmt.machinelearningservices.models.DataContainerEntity] + :ivar continuation_token: The token used in retrieving the next page. If null, there are no + additional pages. + :vartype continuation_token: str + :ivar next_link: The link to the next page constructed using the continuationToken. If null, + there are no additional pages. + :vartype next_link: str + """ + + _attribute_map = { + 'value': {'key': 'value', 'type': '[DataContainerEntity]'}, + 'continuation_token': {'key': 'continuationToken', 'type': 'str'}, + 'next_link': {'key': 'nextLink', 'type': 'str'}, + } + + def __init__( + self, + *, + value: Optional[List["DataContainerEntity"]] = None, + continuation_token: Optional[str] = None, + next_link: Optional[str] = None, + **kwargs + ): + """ + :keyword value: An array of objects of type DataContainerEntity. + :paramtype value: list[~azure.mgmt.machinelearningservices.models.DataContainerEntity] + :keyword continuation_token: The token used in retrieving the next page. If null, there are no + additional pages. + :paramtype continuation_token: str + :keyword next_link: The link to the next page constructed using the continuationToken. If + null, there are no additional pages. + :paramtype next_link: str + """ + super(PaginatedDataContainerEntityList, self).__init__(**kwargs) + self.value = value + self.continuation_token = continuation_token + self.next_link = next_link + + +class PaginatedDatasetDefinitionList(msrest.serialization.Model): + """A paginated list of DatasetDefinitions. + + :ivar value: An array of objects of type DatasetDefinition. + :vartype value: list[~azure.mgmt.machinelearningservices.models.DatasetDefinition] + :ivar continuation_token: The token used in retrieving the next page. If null, there are no + additional pages. + :vartype continuation_token: str + :ivar next_link: The link to the next page constructed using the continuationToken. If null, + there are no additional pages. + :vartype next_link: str + """ + + _attribute_map = { + 'value': {'key': 'value', 'type': '[DatasetDefinition]'}, + 'continuation_token': {'key': 'continuationToken', 'type': 'str'}, + 'next_link': {'key': 'nextLink', 'type': 'str'}, + } + + def __init__( + self, + *, + value: Optional[List["DatasetDefinition"]] = None, + continuation_token: Optional[str] = None, + next_link: Optional[str] = None, + **kwargs + ): + """ + :keyword value: An array of objects of type DatasetDefinition. + :paramtype value: list[~azure.mgmt.machinelearningservices.models.DatasetDefinition] + :keyword continuation_token: The token used in retrieving the next page. If null, there are no + additional pages. + :paramtype continuation_token: str + :keyword next_link: The link to the next page constructed using the continuationToken. If + null, there are no additional pages. + :paramtype next_link: str + """ + super(PaginatedDatasetDefinitionList, self).__init__(**kwargs) + self.value = value + self.continuation_token = continuation_token + self.next_link = next_link + + +class PaginatedDatasetList(msrest.serialization.Model): + """A paginated list of Datasets. + + :ivar value: An array of objects of type Dataset. + :vartype value: list[~azure.mgmt.machinelearningservices.models.Dataset] + :ivar continuation_token: The token used in retrieving the next page. If null, there are no + additional pages. + :vartype continuation_token: str + :ivar next_link: The link to the next page constructed using the continuationToken. If null, + there are no additional pages. + :vartype next_link: str + """ + + _attribute_map = { + 'value': {'key': 'value', 'type': '[Dataset]'}, + 'continuation_token': {'key': 'continuationToken', 'type': 'str'}, + 'next_link': {'key': 'nextLink', 'type': 'str'}, + } + + def __init__( + self, + *, + value: Optional[List["Dataset"]] = None, + continuation_token: Optional[str] = None, + next_link: Optional[str] = None, + **kwargs + ): + """ + :keyword value: An array of objects of type Dataset. + :paramtype value: list[~azure.mgmt.machinelearningservices.models.Dataset] + :keyword continuation_token: The token used in retrieving the next page. If null, there are no + additional pages. + :paramtype continuation_token: str + :keyword next_link: The link to the next page constructed using the continuationToken. If + null, there are no additional pages. + :paramtype next_link: str + """ + super(PaginatedDatasetList, self).__init__(**kwargs) + self.value = value + self.continuation_token = continuation_token + self.next_link = next_link + + +class PaginatedDatasetV2List(msrest.serialization.Model): + """A paginated list of DatasetV2s. + + :ivar value: An array of objects of type DatasetV2. + :vartype value: list[~azure.mgmt.machinelearningservices.models.DatasetV2] + :ivar continuation_token: The token used in retrieving the next page. If null, there are no + additional pages. + :vartype continuation_token: str + :ivar next_link: The link to the next page constructed using the continuationToken. If null, + there are no additional pages. + :vartype next_link: str + """ + + _attribute_map = { + 'value': {'key': 'value', 'type': '[DatasetV2]'}, + 'continuation_token': {'key': 'continuationToken', 'type': 'str'}, + 'next_link': {'key': 'nextLink', 'type': 'str'}, + } + + def __init__( + self, + *, + value: Optional[List["DatasetV2"]] = None, + continuation_token: Optional[str] = None, + next_link: Optional[str] = None, + **kwargs + ): + """ + :keyword value: An array of objects of type DatasetV2. + :paramtype value: list[~azure.mgmt.machinelearningservices.models.DatasetV2] + :keyword continuation_token: The token used in retrieving the next page. If null, there are no + additional pages. + :paramtype continuation_token: str + :keyword next_link: The link to the next page constructed using the continuationToken. If + null, there are no additional pages. + :paramtype next_link: str + """ + super(PaginatedDatasetV2List, self).__init__(**kwargs) + self.value = value + self.continuation_token = continuation_token + self.next_link = next_link + + +class PaginatedDataVersionEntityList(msrest.serialization.Model): + """A paginated list of DataVersionEntitys. + + :ivar value: An array of objects of type DataVersionEntity. + :vartype value: list[~azure.mgmt.machinelearningservices.models.DataVersionEntity] + :ivar continuation_token: The token used in retrieving the next page. If null, there are no + additional pages. + :vartype continuation_token: str + :ivar next_link: The link to the next page constructed using the continuationToken. If null, + there are no additional pages. + :vartype next_link: str + """ + + _attribute_map = { + 'value': {'key': 'value', 'type': '[DataVersionEntity]'}, + 'continuation_token': {'key': 'continuationToken', 'type': 'str'}, + 'next_link': {'key': 'nextLink', 'type': 'str'}, + } + + def __init__( + self, + *, + value: Optional[List["DataVersionEntity"]] = None, + continuation_token: Optional[str] = None, + next_link: Optional[str] = None, + **kwargs + ): + """ + :keyword value: An array of objects of type DataVersionEntity. + :paramtype value: list[~azure.mgmt.machinelearningservices.models.DataVersionEntity] + :keyword continuation_token: The token used in retrieving the next page. If null, there are no + additional pages. + :paramtype continuation_token: str + :keyword next_link: The link to the next page constructed using the continuationToken. If + null, there are no additional pages. + :paramtype next_link: str + """ + super(PaginatedDataVersionEntityList, self).__init__(**kwargs) + self.value = value + self.continuation_token = continuation_token + self.next_link = next_link + + +class PaginatedStringList(msrest.serialization.Model): + """A paginated list of Strings. + + :ivar value: An array of objects of type String. + :vartype value: list[str] + :ivar continuation_token: The token used in retrieving the next page. If null, there are no + additional pages. + :vartype continuation_token: str + :ivar next_link: The link to the next page constructed using the continuationToken. If null, + there are no additional pages. + :vartype next_link: str + """ + + _attribute_map = { + 'value': {'key': 'value', 'type': '[str]'}, + 'continuation_token': {'key': 'continuationToken', 'type': 'str'}, + 'next_link': {'key': 'nextLink', 'type': 'str'}, + } + + def __init__( + self, + *, + value: Optional[List[str]] = None, + continuation_token: Optional[str] = None, + next_link: Optional[str] = None, + **kwargs + ): + """ + :keyword value: An array of objects of type String. + :paramtype value: list[str] + :keyword continuation_token: The token used in retrieving the next page. If null, there are no + additional pages. + :paramtype continuation_token: str + :keyword next_link: The link to the next page constructed using the continuationToken. If + null, there are no additional pages. + :paramtype next_link: str + """ + super(PaginatedStringList, self).__init__(**kwargs) + self.value = value + self.continuation_token = continuation_token + self.next_link = next_link + + +class ProfileActionResult(msrest.serialization.Model): + """ProfileActionResult. + + :ivar profile_action_id: + :vartype profile_action_id: str + :ivar status: + :vartype status: str + :ivar completed_on_utc: + :vartype completed_on_utc: ~datetime.datetime + :ivar action_result: + :vartype action_result: ~azure.mgmt.machinelearningservices.models.ActionResult + """ + + _attribute_map = { + 'profile_action_id': {'key': 'profileActionId', 'type': 'str'}, + 'status': {'key': 'status', 'type': 'str'}, + 'completed_on_utc': {'key': 'completedOnUtc', 'type': 'iso-8601'}, + 'action_result': {'key': 'actionResult', 'type': 'ActionResult'}, + } + + def __init__( + self, + *, + profile_action_id: Optional[str] = None, + status: Optional[str] = None, + completed_on_utc: Optional[datetime.datetime] = None, + action_result: Optional["ActionResult"] = None, + **kwargs + ): + """ + :keyword profile_action_id: + :paramtype profile_action_id: str + :keyword status: + :paramtype status: str + :keyword completed_on_utc: + :paramtype completed_on_utc: ~datetime.datetime + :keyword action_result: + :paramtype action_result: ~azure.mgmt.machinelearningservices.models.ActionResult + """ + super(ProfileActionResult, self).__init__(**kwargs) + self.profile_action_id = profile_action_id + self.status = status + self.completed_on_utc = completed_on_utc + self.action_result = action_result + + +class ProfileResult(msrest.serialization.Model): + """ProfileResult. + + :ivar column_name: + :vartype column_name: str + :ivar type: Possible values include: "String", "Boolean", "Integer", "Decimal", "Date", + "Unknown", "Error", "Null", "DataRow", "List", "Stream". + :vartype type: str or ~azure.mgmt.machinelearningservices.models.FieldType + :ivar min: + :vartype min: ~azure.mgmt.machinelearningservices.models.DataField + :ivar max: + :vartype max: ~azure.mgmt.machinelearningservices.models.DataField + :ivar count: + :vartype count: long + :ivar missing_count: + :vartype missing_count: long + :ivar not_missing_count: + :vartype not_missing_count: long + :ivar percent_missing: + :vartype percent_missing: float + :ivar error_count: + :vartype error_count: long + :ivar empty_count: + :vartype empty_count: long + :ivar quantiles: + :vartype quantiles: ~azure.mgmt.machinelearningservices.models.Quantiles + :ivar whisker_top: + :vartype whisker_top: float + :ivar whisker_bottom: + :vartype whisker_bottom: float + :ivar moments: + :vartype moments: ~azure.mgmt.machinelearningservices.models.Moments + :ivar type_counts: + :vartype type_counts: list[~azure.mgmt.machinelearningservices.models.TypeCount] + :ivar value_counts: + :vartype value_counts: list[~azure.mgmt.machinelearningservices.models.ValueCount] + :ivar unique_values: + :vartype unique_values: long + :ivar histogram: + :vartype histogram: list[~azure.mgmt.machinelearningservices.models.HistogramBin] + :ivar s_type_counts: + :vartype s_type_counts: list[~azure.mgmt.machinelearningservices.models.STypeCount] + :ivar average_spaces_count: + :vartype average_spaces_count: float + :ivar string_lengths: + :vartype string_lengths: list[~azure.mgmt.machinelearningservices.models.StringLengthCount] + """ + + _attribute_map = { + 'column_name': {'key': 'columnName', 'type': 'str'}, + 'type': {'key': 'type', 'type': 'str'}, + 'min': {'key': 'min', 'type': 'DataField'}, + 'max': {'key': 'max', 'type': 'DataField'}, + 'count': {'key': 'count', 'type': 'long'}, + 'missing_count': {'key': 'missingCount', 'type': 'long'}, + 'not_missing_count': {'key': 'notMissingCount', 'type': 'long'}, + 'percent_missing': {'key': 'percentMissing', 'type': 'float'}, + 'error_count': {'key': 'errorCount', 'type': 'long'}, + 'empty_count': {'key': 'emptyCount', 'type': 'long'}, + 'quantiles': {'key': 'quantiles', 'type': 'Quantiles'}, + 'whisker_top': {'key': 'whiskerTop', 'type': 'float'}, + 'whisker_bottom': {'key': 'whiskerBottom', 'type': 'float'}, + 'moments': {'key': 'moments', 'type': 'Moments'}, + 'type_counts': {'key': 'typeCounts', 'type': '[TypeCount]'}, + 'value_counts': {'key': 'valueCounts', 'type': '[ValueCount]'}, + 'unique_values': {'key': 'uniqueValues', 'type': 'long'}, + 'histogram': {'key': 'histogram', 'type': '[HistogramBin]'}, + 's_type_counts': {'key': 'sTypeCounts', 'type': '[STypeCount]'}, + 'average_spaces_count': {'key': 'averageSpacesCount', 'type': 'float'}, + 'string_lengths': {'key': 'stringLengths', 'type': '[StringLengthCount]'}, + } + + def __init__( + self, + *, + column_name: Optional[str] = None, + type: Optional[Union[str, "FieldType"]] = None, + min: Optional["DataField"] = None, + max: Optional["DataField"] = None, + count: Optional[int] = None, + missing_count: Optional[int] = None, + not_missing_count: Optional[int] = None, + percent_missing: Optional[float] = None, + error_count: Optional[int] = None, + empty_count: Optional[int] = None, + quantiles: Optional["Quantiles"] = None, + whisker_top: Optional[float] = None, + whisker_bottom: Optional[float] = None, + moments: Optional["Moments"] = None, + type_counts: Optional[List["TypeCount"]] = None, + value_counts: Optional[List["ValueCount"]] = None, + unique_values: Optional[int] = None, + histogram: Optional[List["HistogramBin"]] = None, + s_type_counts: Optional[List["STypeCount"]] = None, + average_spaces_count: Optional[float] = None, + string_lengths: Optional[List["StringLengthCount"]] = None, + **kwargs + ): + """ + :keyword column_name: + :paramtype column_name: str + :keyword type: Possible values include: "String", "Boolean", "Integer", "Decimal", "Date", + "Unknown", "Error", "Null", "DataRow", "List", "Stream". + :paramtype type: str or ~azure.mgmt.machinelearningservices.models.FieldType + :keyword min: + :paramtype min: ~azure.mgmt.machinelearningservices.models.DataField + :keyword max: + :paramtype max: ~azure.mgmt.machinelearningservices.models.DataField + :keyword count: + :paramtype count: long + :keyword missing_count: + :paramtype missing_count: long + :keyword not_missing_count: + :paramtype not_missing_count: long + :keyword percent_missing: + :paramtype percent_missing: float + :keyword error_count: + :paramtype error_count: long + :keyword empty_count: + :paramtype empty_count: long + :keyword quantiles: + :paramtype quantiles: ~azure.mgmt.machinelearningservices.models.Quantiles + :keyword whisker_top: + :paramtype whisker_top: float + :keyword whisker_bottom: + :paramtype whisker_bottom: float + :keyword moments: + :paramtype moments: ~azure.mgmt.machinelearningservices.models.Moments + :keyword type_counts: + :paramtype type_counts: list[~azure.mgmt.machinelearningservices.models.TypeCount] + :keyword value_counts: + :paramtype value_counts: list[~azure.mgmt.machinelearningservices.models.ValueCount] + :keyword unique_values: + :paramtype unique_values: long + :keyword histogram: + :paramtype histogram: list[~azure.mgmt.machinelearningservices.models.HistogramBin] + :keyword s_type_counts: + :paramtype s_type_counts: list[~azure.mgmt.machinelearningservices.models.STypeCount] + :keyword average_spaces_count: + :paramtype average_spaces_count: float + :keyword string_lengths: + :paramtype string_lengths: list[~azure.mgmt.machinelearningservices.models.StringLengthCount] + """ + super(ProfileResult, self).__init__(**kwargs) + self.column_name = column_name + self.type = type + self.min = min + self.max = max + self.count = count + self.missing_count = missing_count + self.not_missing_count = not_missing_count + self.percent_missing = percent_missing + self.error_count = error_count + self.empty_count = empty_count + self.quantiles = quantiles + self.whisker_top = whisker_top + self.whisker_bottom = whisker_bottom + self.moments = moments + self.type_counts = type_counts + self.value_counts = value_counts + self.unique_values = unique_values + self.histogram = histogram + self.s_type_counts = s_type_counts + self.average_spaces_count = average_spaces_count + self.string_lengths = string_lengths + + +class Quantiles(msrest.serialization.Model): + """Quantiles. + + :ivar p0_d1: + :vartype p0_d1: float + :ivar p1: + :vartype p1: float + :ivar p5: + :vartype p5: float + :ivar p25: + :vartype p25: float + :ivar p50: + :vartype p50: float + :ivar p75: + :vartype p75: float + :ivar p95: + :vartype p95: float + :ivar p99: + :vartype p99: float + :ivar p99_d9: + :vartype p99_d9: float + """ + + _attribute_map = { + 'p0_d1': {'key': 'p0D1', 'type': 'float'}, + 'p1': {'key': 'p1', 'type': 'float'}, + 'p5': {'key': 'p5', 'type': 'float'}, + 'p25': {'key': 'p25', 'type': 'float'}, + 'p50': {'key': 'p50', 'type': 'float'}, + 'p75': {'key': 'p75', 'type': 'float'}, + 'p95': {'key': 'p95', 'type': 'float'}, + 'p99': {'key': 'p99', 'type': 'float'}, + 'p99_d9': {'key': 'p99D9', 'type': 'float'}, + } + + def __init__( + self, + *, + p0_d1: Optional[float] = None, + p1: Optional[float] = None, + p5: Optional[float] = None, + p25: Optional[float] = None, + p50: Optional[float] = None, + p75: Optional[float] = None, + p95: Optional[float] = None, + p99: Optional[float] = None, + p99_d9: Optional[float] = None, + **kwargs + ): + """ + :keyword p0_d1: + :paramtype p0_d1: float + :keyword p1: + :paramtype p1: float + :keyword p5: + :paramtype p5: float + :keyword p25: + :paramtype p25: float + :keyword p50: + :paramtype p50: float + :keyword p75: + :paramtype p75: float + :keyword p95: + :paramtype p95: float + :keyword p99: + :paramtype p99: float + :keyword p99_d9: + :paramtype p99_d9: float + """ + super(Quantiles, self).__init__(**kwargs) + self.p0_d1 = p0_d1 + self.p1 = p1 + self.p5 = p5 + self.p25 = p25 + self.p50 = p50 + self.p75 = p75 + self.p95 = p95 + self.p99 = p99 + self.p99_d9 = p99_d9 + + +class RegisterExistingData(msrest.serialization.Model): + """RegisterExistingData. + + All required parameters must be populated in order to send to Azure. + + :ivar existing_unregistered_asset_id: Required. + :vartype existing_unregistered_asset_id: str + :ivar name: Required. + :vartype name: str + :ivar version: + :vartype version: str + """ + + _validation = { + 'existing_unregistered_asset_id': {'required': True}, + 'name': {'required': True}, + } + + _attribute_map = { + 'existing_unregistered_asset_id': {'key': 'existingUnregisteredAssetId', 'type': 'str'}, + 'name': {'key': 'name', 'type': 'str'}, + 'version': {'key': 'version', 'type': 'str'}, + } + + def __init__( + self, + *, + existing_unregistered_asset_id: str, + name: str, + version: Optional[str] = None, + **kwargs + ): + """ + :keyword existing_unregistered_asset_id: Required. + :paramtype existing_unregistered_asset_id: str + :keyword name: Required. + :paramtype name: str + :keyword version: + :paramtype version: str + """ + super(RegisterExistingData, self).__init__(**kwargs) + self.existing_unregistered_asset_id = existing_unregistered_asset_id + self.name = name + self.version = version + + +class RootError(msrest.serialization.Model): + """The root error. + + :ivar code: The service-defined error code. Supported error codes: ServiceError, UserError, + ValidationError, AzureStorageError, TransientError, RequestThrottled. + :vartype code: str + :ivar severity: The Severity of error. + :vartype severity: int + :ivar message: A human-readable representation of the error. + :vartype message: str + :ivar message_format: An unformatted version of the message with no variable substitution. + :vartype message_format: str + :ivar message_parameters: Value substitutions corresponding to the contents of MessageFormat. + :vartype message_parameters: dict[str, str] + :ivar reference_code: This code can optionally be set by the system generating the error. + It should be used to classify the problem and identify the module and code area where the + failure occured. + :vartype reference_code: str + :ivar details_uri: A URI which points to more details about the context of the error. + :vartype details_uri: str + :ivar target: The target of the error (e.g., the name of the property in error). + :vartype target: str + :ivar details: The related errors that occurred during the request. + :vartype details: list[~azure.mgmt.machinelearningservices.models.RootError] + :ivar inner_error: A nested structure of errors. + :vartype inner_error: ~azure.mgmt.machinelearningservices.models.InnerErrorResponse + :ivar additional_info: The error additional info. + :vartype additional_info: list[~azure.mgmt.machinelearningservices.models.ErrorAdditionalInfo] + """ + + _attribute_map = { + 'code': {'key': 'code', 'type': 'str'}, + 'severity': {'key': 'severity', 'type': 'int'}, + 'message': {'key': 'message', 'type': 'str'}, + 'message_format': {'key': 'messageFormat', 'type': 'str'}, + 'message_parameters': {'key': 'messageParameters', 'type': '{str}'}, + 'reference_code': {'key': 'referenceCode', 'type': 'str'}, + 'details_uri': {'key': 'detailsUri', 'type': 'str'}, + 'target': {'key': 'target', 'type': 'str'}, + 'details': {'key': 'details', 'type': '[RootError]'}, + 'inner_error': {'key': 'innerError', 'type': 'InnerErrorResponse'}, + 'additional_info': {'key': 'additionalInfo', 'type': '[ErrorAdditionalInfo]'}, + } + + def __init__( + self, + *, + code: Optional[str] = None, + severity: Optional[int] = None, + message: Optional[str] = None, + message_format: Optional[str] = None, + message_parameters: Optional[Dict[str, str]] = None, + reference_code: Optional[str] = None, + details_uri: Optional[str] = None, + target: Optional[str] = None, + details: Optional[List["RootError"]] = None, + inner_error: Optional["InnerErrorResponse"] = None, + additional_info: Optional[List["ErrorAdditionalInfo"]] = None, + **kwargs + ): + """ + :keyword code: The service-defined error code. Supported error codes: ServiceError, UserError, + ValidationError, AzureStorageError, TransientError, RequestThrottled. + :paramtype code: str + :keyword severity: The Severity of error. + :paramtype severity: int + :keyword message: A human-readable representation of the error. + :paramtype message: str + :keyword message_format: An unformatted version of the message with no variable substitution. + :paramtype message_format: str + :keyword message_parameters: Value substitutions corresponding to the contents of + MessageFormat. + :paramtype message_parameters: dict[str, str] + :keyword reference_code: This code can optionally be set by the system generating the error. + It should be used to classify the problem and identify the module and code area where the + failure occured. + :paramtype reference_code: str + :keyword details_uri: A URI which points to more details about the context of the error. + :paramtype details_uri: str + :keyword target: The target of the error (e.g., the name of the property in error). + :paramtype target: str + :keyword details: The related errors that occurred during the request. + :paramtype details: list[~azure.mgmt.machinelearningservices.models.RootError] + :keyword inner_error: A nested structure of errors. + :paramtype inner_error: ~azure.mgmt.machinelearningservices.models.InnerErrorResponse + :keyword additional_info: The error additional info. + :paramtype additional_info: + list[~azure.mgmt.machinelearningservices.models.ErrorAdditionalInfo] + """ + super(RootError, self).__init__(**kwargs) + self.code = code + self.severity = severity + self.message = message + self.message_format = message_format + self.message_parameters = message_parameters + self.reference_code = reference_code + self.details_uri = details_uri + self.target = target + self.details = details + self.inner_error = inner_error + self.additional_info = additional_info + + +class SqlDataPath(msrest.serialization.Model): + """SqlDataPath. + + :ivar sql_table_name: + :vartype sql_table_name: str + :ivar sql_query: + :vartype sql_query: str + :ivar sql_stored_procedure_name: + :vartype sql_stored_procedure_name: str + :ivar sql_stored_procedure_params: + :vartype sql_stored_procedure_params: + list[~azure.mgmt.machinelearningservices.models.StoredProcedureParameter] + :ivar query_timeout: + :vartype query_timeout: long + """ + + _attribute_map = { + 'sql_table_name': {'key': 'sqlTableName', 'type': 'str'}, + 'sql_query': {'key': 'sqlQuery', 'type': 'str'}, + 'sql_stored_procedure_name': {'key': 'sqlStoredProcedureName', 'type': 'str'}, + 'sql_stored_procedure_params': {'key': 'sqlStoredProcedureParams', 'type': '[StoredProcedureParameter]'}, + 'query_timeout': {'key': 'queryTimeout', 'type': 'long'}, + } + + def __init__( + self, + *, + sql_table_name: Optional[str] = None, + sql_query: Optional[str] = None, + sql_stored_procedure_name: Optional[str] = None, + sql_stored_procedure_params: Optional[List["StoredProcedureParameter"]] = None, + query_timeout: Optional[int] = None, + **kwargs + ): + """ + :keyword sql_table_name: + :paramtype sql_table_name: str + :keyword sql_query: + :paramtype sql_query: str + :keyword sql_stored_procedure_name: + :paramtype sql_stored_procedure_name: str + :keyword sql_stored_procedure_params: + :paramtype sql_stored_procedure_params: + list[~azure.mgmt.machinelearningservices.models.StoredProcedureParameter] + :keyword query_timeout: + :paramtype query_timeout: long + """ + super(SqlDataPath, self).__init__(**kwargs) + self.sql_table_name = sql_table_name + self.sql_query = sql_query + self.sql_stored_procedure_name = sql_stored_procedure_name + self.sql_stored_procedure_params = sql_stored_procedure_params + self.query_timeout = query_timeout + + +class StoredProcedureParameter(msrest.serialization.Model): + """StoredProcedureParameter. + + :ivar name: + :vartype name: str + :ivar value: + :vartype value: str + :ivar type: Possible values include: "String", "Int", "Decimal", "Guid", "Boolean", "Date". + :vartype type: str or ~azure.mgmt.machinelearningservices.models.StoredProcedureParameterType + """ + + _attribute_map = { + 'name': {'key': 'name', 'type': 'str'}, + 'value': {'key': 'value', 'type': 'str'}, + 'type': {'key': 'type', 'type': 'str'}, + } + + def __init__( + self, + *, + name: Optional[str] = None, + value: Optional[str] = None, + type: Optional[Union[str, "StoredProcedureParameterType"]] = None, + **kwargs + ): + """ + :keyword name: + :paramtype name: str + :keyword value: + :paramtype value: str + :keyword type: Possible values include: "String", "Int", "Decimal", "Guid", "Boolean", "Date". + :paramtype type: str or ~azure.mgmt.machinelearningservices.models.StoredProcedureParameterType + """ + super(StoredProcedureParameter, self).__init__(**kwargs) + self.name = name + self.value = value + self.type = type + + +class StringLengthCount(msrest.serialization.Model): + """StringLengthCount. + + :ivar length: + :vartype length: long + :ivar count: + :vartype count: long + """ + + _attribute_map = { + 'length': {'key': 'length', 'type': 'long'}, + 'count': {'key': 'count', 'type': 'long'}, + } + + def __init__( + self, + *, + length: Optional[int] = None, + count: Optional[int] = None, + **kwargs + ): + """ + :keyword length: + :paramtype length: long + :keyword count: + :paramtype count: long + """ + super(StringLengthCount, self).__init__(**kwargs) + self.length = length + self.count = count + + +class STypeCount(msrest.serialization.Model): + """STypeCount. + + :ivar s_type: Possible values include: "EmailAddress", "GeographicCoordinate", "Ipv4Address", + "Ipv6Address", "UsPhoneNumber", "ZipCode". + :vartype s_type: str or ~azure.mgmt.machinelearningservices.models.SType + :ivar count: + :vartype count: long + """ + + _attribute_map = { + 's_type': {'key': 'sType', 'type': 'str'}, + 'count': {'key': 'count', 'type': 'long'}, + } + + def __init__( + self, + *, + s_type: Optional[Union[str, "SType"]] = None, + count: Optional[int] = None, + **kwargs + ): + """ + :keyword s_type: Possible values include: "EmailAddress", "GeographicCoordinate", + "Ipv4Address", "Ipv6Address", "UsPhoneNumber", "ZipCode". + :paramtype s_type: str or ~azure.mgmt.machinelearningservices.models.SType + :keyword count: + :paramtype count: long + """ + super(STypeCount, self).__init__(**kwargs) + self.s_type = s_type + self.count = count + + +class TypeCount(msrest.serialization.Model): + """TypeCount. + + :ivar type: Possible values include: "String", "Boolean", "Integer", "Decimal", "Date", + "Unknown", "Error", "Null", "DataRow", "List", "Stream". + :vartype type: str or ~azure.mgmt.machinelearningservices.models.FieldType + :ivar count: + :vartype count: long + """ + + _attribute_map = { + 'type': {'key': 'type', 'type': 'str'}, + 'count': {'key': 'count', 'type': 'long'}, + } + + def __init__( + self, + *, + type: Optional[Union[str, "FieldType"]] = None, + count: Optional[int] = None, + **kwargs + ): + """ + :keyword type: Possible values include: "String", "Boolean", "Integer", "Decimal", "Date", + "Unknown", "Error", "Null", "DataRow", "List", "Stream". + :paramtype type: str or ~azure.mgmt.machinelearningservices.models.FieldType + :keyword count: + :paramtype count: long + """ + super(TypeCount, self).__init__(**kwargs) + self.type = type + self.count = count + + +class User(msrest.serialization.Model): + """User. + + :ivar user_object_id: A user or service principal's object ID. + This is EUPI and may only be logged to warm path telemetry. + :vartype user_object_id: str + :ivar user_pu_id: A user or service principal's PuID. + This is PII and should never be logged. + :vartype user_pu_id: str + :ivar user_idp: A user identity provider. Eg live.com + This is PII and should never be logged. + :vartype user_idp: str + :ivar user_alt_sec_id: A user alternate sec id. This represents the user in a different + identity provider system Eg.1:live.com:puid + This is PII and should never be logged. + :vartype user_alt_sec_id: str + :ivar user_iss: The issuer which issed the token for this user. + This is PII and should never be logged. + :vartype user_iss: str + :ivar user_tenant_id: A user or service principal's tenant ID. + :vartype user_tenant_id: str + :ivar user_name: A user's full name or a service principal's app ID. + This is PII and should never be logged. + :vartype user_name: str + :ivar upn: A user's Principal name (upn) + This is PII andshould never be logged. + :vartype upn: str + """ + + _attribute_map = { + 'user_object_id': {'key': 'userObjectId', 'type': 'str'}, + 'user_pu_id': {'key': 'userPuId', 'type': 'str'}, + 'user_idp': {'key': 'userIdp', 'type': 'str'}, + 'user_alt_sec_id': {'key': 'userAltSecId', 'type': 'str'}, + 'user_iss': {'key': 'userIss', 'type': 'str'}, + 'user_tenant_id': {'key': 'userTenantId', 'type': 'str'}, + 'user_name': {'key': 'userName', 'type': 'str'}, + 'upn': {'key': 'upn', 'type': 'str'}, + } + + def __init__( + self, + *, + user_object_id: Optional[str] = None, + user_pu_id: Optional[str] = None, + user_idp: Optional[str] = None, + user_alt_sec_id: Optional[str] = None, + user_iss: Optional[str] = None, + user_tenant_id: Optional[str] = None, + user_name: Optional[str] = None, + upn: Optional[str] = None, + **kwargs + ): + """ + :keyword user_object_id: A user or service principal's object ID. + This is EUPI and may only be logged to warm path telemetry. + :paramtype user_object_id: str + :keyword user_pu_id: A user or service principal's PuID. + This is PII and should never be logged. + :paramtype user_pu_id: str + :keyword user_idp: A user identity provider. Eg live.com + This is PII and should never be logged. + :paramtype user_idp: str + :keyword user_alt_sec_id: A user alternate sec id. This represents the user in a different + identity provider system Eg.1:live.com:puid + This is PII and should never be logged. + :paramtype user_alt_sec_id: str + :keyword user_iss: The issuer which issed the token for this user. + This is PII and should never be logged. + :paramtype user_iss: str + :keyword user_tenant_id: A user or service principal's tenant ID. + :paramtype user_tenant_id: str + :keyword user_name: A user's full name or a service principal's app ID. + This is PII and should never be logged. + :paramtype user_name: str + :keyword upn: A user's Principal name (upn) + This is PII andshould never be logged. + :paramtype upn: str + """ + super(User, self).__init__(**kwargs) + self.user_object_id = user_object_id + self.user_pu_id = user_pu_id + self.user_idp = user_idp + self.user_alt_sec_id = user_alt_sec_id + self.user_iss = user_iss + self.user_tenant_id = user_tenant_id + self.user_name = user_name + self.upn = upn + + +class ValueCount(msrest.serialization.Model): + """ValueCount. + + :ivar value: + :vartype value: ~azure.mgmt.machinelearningservices.models.DataField + :ivar count: + :vartype count: long + """ + + _attribute_map = { + 'value': {'key': 'value', 'type': 'DataField'}, + 'count': {'key': 'count', 'type': 'long'}, + } + + def __init__( + self, + *, + value: Optional["DataField"] = None, + count: Optional[int] = None, + **kwargs + ): + """ + :keyword value: + :paramtype value: ~azure.mgmt.machinelearningservices.models.DataField + :keyword count: + :paramtype count: long + """ + super(ValueCount, self).__init__(**kwargs) + self.value = value + self.count = count diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/__init__.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/__init__.py new file mode 100644 index 00000000..f0340813 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/__init__.py @@ -0,0 +1,27 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) AutoRest Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +from ._data_call_operations import DataCallOperations +from ._data_container_operations import DataContainerOperations +from ._delete_operations import DeleteOperations +from ._datasets_v1_operations import DatasetsV1Operations +from ._dataset_controller_v2_operations import DatasetControllerV2Operations +from ._dataset_v2_operations import DatasetV2Operations +from ._data_version_operations import DataVersionOperations +from ._get_operation_status_operations import GetOperationStatusOperations + +__all__ = [ + 'DataCallOperations', + 'DataContainerOperations', + 'DeleteOperations', + 'DatasetsV1Operations', + 'DatasetControllerV2Operations', + 'DatasetV2Operations', + 'DataVersionOperations', + 'GetOperationStatusOperations', +] diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/_data_call_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/_data_call_operations.py new file mode 100644 index 00000000..4e7865d1 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/_data_call_operations.py @@ -0,0 +1,356 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) AutoRest Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +import functools +from typing import TYPE_CHECKING +import warnings + +from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error +from azure.core.pipeline import PipelineResponse +from azure.core.pipeline.transport import HttpResponse +from azure.core.rest import HttpRequest +from azure.core.tracing.decorator import distributed_trace +from azure.mgmt.core.exceptions import ARMErrorFormat +from msrest import Serializer + +from .. import models as _models +from .._vendor import _convert_request, _format_url_section + +if TYPE_CHECKING: + # pylint: disable=unused-import,ungrouped-imports + from typing import Any, Callable, Dict, Generic, List, Optional, TypeVar + T = TypeVar('T') + ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]] + +_SERIALIZER = Serializer() +_SERIALIZER.client_side_validation = False +# fmt: off + +def build_get_schema_for_ml_table_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + content_type = kwargs.pop('content_type', None) # type: Optional[str] + + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/data/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datacall/schema') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + if content_type is not None: + header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str') + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="POST", + url=url, + headers=header_parameters, + **kwargs + ) + + +def build_get_preview_for_ml_table_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + content_type = kwargs.pop('content_type', None) # type: Optional[str] + + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/data/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datacall/preview') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + if content_type is not None: + header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str') + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="POST", + url=url, + headers=header_parameters, + **kwargs + ) + + +def build_get_quick_profile_for_ml_table_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + content_type = kwargs.pop('content_type', None) # type: Optional[str] + + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/data/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datacall/quickprofile') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + if content_type is not None: + header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str') + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="POST", + url=url, + headers=header_parameters, + **kwargs + ) + +# fmt: on +class DataCallOperations(object): + """DataCallOperations operations. + + You should not instantiate this class directly. Instead, you should create a Client instance that + instantiates it for you and attaches it as an attribute. + + :ivar models: Alias to model classes used in this operation group. + :type models: ~azure.mgmt.machinelearningservices.models + :param client: Client for service requests. + :param config: Configuration of service client. + :param serializer: An object model serializer. + :param deserializer: An object model deserializer. + """ + + models = _models + + def __init__(self, client, config, serializer, deserializer): + self._client = client + self._serialize = serializer + self._deserialize = deserializer + self._config = config + + @distributed_trace + def get_schema_for_ml_table( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + body=None, # type: Optional["_models.DataCallRequest"] + **kwargs # type: Any + ): + # type: (...) -> List["_models.ColumnDefinition"] + """Get schema for a specific MLTable. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.DataCallRequest + :keyword callable cls: A custom type or function that will be passed the direct response + :return: list of ColumnDefinition, or the result of cls(response) + :rtype: list[~azure.mgmt.machinelearningservices.models.ColumnDefinition] + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType[List["_models.ColumnDefinition"]] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'DataCallRequest') + else: + _json = None + + request = build_get_schema_for_ml_table_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + content_type=content_type, + json=_json, + template_url=self.get_schema_for_ml_table.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('[ColumnDefinition]', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + get_schema_for_ml_table.metadata = {'url': '/data/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datacall/schema'} # type: ignore + + + @distributed_trace + def get_preview_for_ml_table( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + body=None, # type: Optional["_models.DataCallRequest"] + **kwargs # type: Any + ): + # type: (...) -> "_models.DataViewSetResult" + """Get preview for a specific MLTable. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.DataCallRequest + :keyword callable cls: A custom type or function that will be passed the direct response + :return: DataViewSetResult, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.DataViewSetResult + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.DataViewSetResult"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'DataCallRequest') + else: + _json = None + + request = build_get_preview_for_ml_table_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + content_type=content_type, + json=_json, + template_url=self.get_preview_for_ml_table.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('DataViewSetResult', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + get_preview_for_ml_table.metadata = {'url': '/data/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datacall/preview'} # type: ignore + + + @distributed_trace + def get_quick_profile_for_ml_table( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + body=None, # type: Optional["_models.DataCallRequest"] + **kwargs # type: Any + ): + # type: (...) -> List["_models.ProfileResult"] + """Get quick profile for a specific MLTable. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.DataCallRequest + :keyword callable cls: A custom type or function that will be passed the direct response + :return: list of ProfileResult, or the result of cls(response) + :rtype: list[~azure.mgmt.machinelearningservices.models.ProfileResult] + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType[List["_models.ProfileResult"]] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'DataCallRequest') + else: + _json = None + + request = build_get_quick_profile_for_ml_table_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + content_type=content_type, + json=_json, + template_url=self.get_quick_profile_for_ml_table.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('[ProfileResult]', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + get_quick_profile_for_ml_table.metadata = {'url': '/data/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datacall/quickprofile'} # type: ignore + diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/_data_container_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/_data_container_operations.py new file mode 100644 index 00000000..ac3af23d --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/_data_container_operations.py @@ -0,0 +1,464 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) AutoRest Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +import functools +from typing import TYPE_CHECKING +import warnings + +from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error +from azure.core.paging import ItemPaged +from azure.core.pipeline import PipelineResponse +from azure.core.pipeline.transport import HttpResponse +from azure.core.rest import HttpRequest +from azure.core.tracing.decorator import distributed_trace +from azure.mgmt.core.exceptions import ARMErrorFormat +from msrest import Serializer + +from .. import models as _models +from .._vendor import _convert_request, _format_url_section + +if TYPE_CHECKING: + # pylint: disable=unused-import,ungrouped-imports + from typing import Any, Callable, Dict, Generic, Iterable, Optional, TypeVar + T = TypeVar('T') + ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]] + +_SERIALIZER = Serializer() +_SERIALIZER.client_side_validation = False +# fmt: off + +def build_create_data_container_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + content_type = kwargs.pop('content_type', None) # type: Optional[str] + + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datacontainer') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + if content_type is not None: + header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str') + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="POST", + url=url, + headers=header_parameters, + **kwargs + ) + + +def build_list_data_container_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datacontainer') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="GET", + url=url, + headers=header_parameters, + **kwargs + ) + + +def build_get_data_container_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + name, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datacontainer/{name}') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + "name": _SERIALIZER.url("name", name, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="GET", + url=url, + headers=header_parameters, + **kwargs + ) + + +def build_modify_data_container_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + name, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + content_type = kwargs.pop('content_type', None) # type: Optional[str] + + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datacontainer/{name}') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + "name": _SERIALIZER.url("name", name, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + if content_type is not None: + header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str') + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="PATCH", + url=url, + headers=header_parameters, + **kwargs + ) + +# fmt: on +class DataContainerOperations(object): + """DataContainerOperations operations. + + You should not instantiate this class directly. Instead, you should create a Client instance that + instantiates it for you and attaches it as an attribute. + + :ivar models: Alias to model classes used in this operation group. + :type models: ~azure.mgmt.machinelearningservices.models + :param client: Client for service requests. + :param config: Configuration of service client. + :param serializer: An object model serializer. + :param deserializer: An object model deserializer. + """ + + models = _models + + def __init__(self, client, config, serializer, deserializer): + self._client = client + self._serialize = serializer + self._deserialize = deserializer + self._config = config + + @distributed_trace + def create_data_container( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + body=None, # type: Optional["_models.DataContainer"] + **kwargs # type: Any + ): + # type: (...) -> "_models.DataContainerEntity" + """create_data_container. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.DataContainer + :keyword callable cls: A custom type or function that will be passed the direct response + :return: DataContainerEntity, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.DataContainerEntity + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.DataContainerEntity"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'DataContainer') + else: + _json = None + + request = build_create_data_container_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + content_type=content_type, + json=_json, + template_url=self.create_data_container.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('DataContainerEntity', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + create_data_container.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datacontainer'} # type: ignore + + + @distributed_trace + def list_data_container( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + **kwargs # type: Any + ): + # type: (...) -> Iterable["_models.PaginatedDataContainerEntityList"] + """list_data_container. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :keyword callable cls: A custom type or function that will be passed the direct response + :return: An iterator like instance of either PaginatedDataContainerEntityList or the result of + cls(response) + :rtype: + ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.PaginatedDataContainerEntityList] + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.PaginatedDataContainerEntityList"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + def prepare_request(next_link=None): + if not next_link: + + request = build_list_data_container_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + template_url=self.list_data_container.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + else: + + request = build_list_data_container_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + template_url=next_link, + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + request.method = "GET" + return request + + def extract_data(pipeline_response): + deserialized = self._deserialize("PaginatedDataContainerEntityList", pipeline_response) + list_of_elem = deserialized.value + if cls: + list_of_elem = cls(list_of_elem) + return deserialized.next_link or None, iter(list_of_elem) + + def get_next(next_link=None): + request = prepare_request(next_link) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + return pipeline_response + + + return ItemPaged( + get_next, extract_data + ) + list_data_container.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datacontainer'} # type: ignore + + @distributed_trace + def get_data_container( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + name, # type: str + **kwargs # type: Any + ): + # type: (...) -> "_models.DataContainerEntity" + """get_data_container. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param name: + :type name: str + :keyword callable cls: A custom type or function that will be passed the direct response + :return: DataContainerEntity, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.DataContainerEntity + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.DataContainerEntity"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + + request = build_get_data_container_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + name=name, + template_url=self.get_data_container.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('DataContainerEntity', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + get_data_container.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datacontainer/{name}'} # type: ignore + + + @distributed_trace + def modify_data_container( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + name, # type: str + body=None, # type: Optional["_models.DataContainerMutable"] + **kwargs # type: Any + ): + # type: (...) -> "_models.DataContainerEntity" + """modify_data_container. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param name: + :type name: str + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.DataContainerMutable + :keyword callable cls: A custom type or function that will be passed the direct response + :return: DataContainerEntity, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.DataContainerEntity + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.DataContainerEntity"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'DataContainerMutable') + else: + _json = None + + request = build_modify_data_container_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + name=name, + content_type=content_type, + json=_json, + template_url=self.modify_data_container.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('DataContainerEntity', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + modify_data_container.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datacontainer/{name}'} # type: ignore + diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/_data_version_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/_data_version_operations.py new file mode 100644 index 00000000..ae87ad4b --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/_data_version_operations.py @@ -0,0 +1,1211 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) AutoRest Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +import functools +from typing import TYPE_CHECKING +import warnings + +from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error +from azure.core.paging import ItemPaged +from azure.core.pipeline import PipelineResponse +from azure.core.pipeline.transport import HttpResponse +from azure.core.rest import HttpRequest +from azure.core.tracing.decorator import distributed_trace +from azure.mgmt.core.exceptions import ARMErrorFormat +from msrest import Serializer + +from .. import models as _models +from .._vendor import _convert_request, _format_url_section + +if TYPE_CHECKING: + # pylint: disable=unused-import,ungrouped-imports + from typing import Any, Callable, Dict, Generic, Iterable, Optional, TypeVar + T = TypeVar('T') + ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]] + +_SERIALIZER = Serializer() +_SERIALIZER.client_side_validation = False +# fmt: off + +def build_create_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + name, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + content_type = kwargs.pop('content_type', None) # type: Optional[str] + + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/{name}/versions') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + "name": _SERIALIZER.url("name", name, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + if content_type is not None: + header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str') + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="POST", + url=url, + headers=header_parameters, + **kwargs + ) + + +def build_list_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + name, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + order_by = kwargs.pop('order_by', None) # type: Optional[str] + top = kwargs.pop('top', None) # type: Optional[int] + + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/{name}/versions') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + "name": _SERIALIZER.url("name", name, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct parameters + query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] + if order_by is not None: + query_parameters['orderBy'] = _SERIALIZER.query("order_by", order_by, 'str') + if top is not None: + query_parameters['top'] = _SERIALIZER.query("top", top, 'int') + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="GET", + url=url, + params=query_parameters, + headers=header_parameters, + **kwargs + ) + + +def build_get_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + name, # type: str + version, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/{name}/versions/{version}') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + "name": _SERIALIZER.url("name", name, 'str'), + "version": _SERIALIZER.url("version", version, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="GET", + url=url, + headers=header_parameters, + **kwargs + ) + + +def build_modify_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + name, # type: str + version, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + content_type = kwargs.pop('content_type', None) # type: Optional[str] + + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/{name}/versions/{version}') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + "name": _SERIALIZER.url("name", name, 'str'), + "version": _SERIALIZER.url("version", version, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + if content_type is not None: + header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str') + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="PATCH", + url=url, + headers=header_parameters, + **kwargs + ) + + +def build_delete_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + name, # type: str + version, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/{name}/versions/{version}') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + "name": _SERIALIZER.url("name", name, 'str'), + "version": _SERIALIZER.url("version", version, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="DELETE", + url=url, + headers=header_parameters, + **kwargs + ) + + +def build_exists_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + name, # type: str + version, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/{name}/versions/{version}/exists') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + "name": _SERIALIZER.url("name", name, 'str'), + "version": _SERIALIZER.url("version", version, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="GET", + url=url, + headers=header_parameters, + **kwargs + ) + + +def build_get_by_asset_id_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + content_type = kwargs.pop('content_type', None) # type: Optional[str] + + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/getByAssetId') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + if content_type is not None: + header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str') + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="POST", + url=url, + headers=header_parameters, + **kwargs + ) + + +def build_create_unregistered_input_data_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + content_type = kwargs.pop('content_type', None) # type: Optional[str] + + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/createUnregisteredInput') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + if content_type is not None: + header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str') + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="POST", + url=url, + headers=header_parameters, + **kwargs + ) + + +def build_create_unregistered_output_data_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + content_type = kwargs.pop('content_type', None) # type: Optional[str] + + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/createUnregisteredOutput') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + if content_type is not None: + header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str') + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="POST", + url=url, + headers=header_parameters, + **kwargs + ) + + +def build_registered_existing_data_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + content_type = kwargs.pop('content_type', None) # type: Optional[str] + + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/registerExisting') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + if content_type is not None: + header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str') + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="POST", + url=url, + headers=header_parameters, + **kwargs + ) + + +def build_batch_get_resolved_uris_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + content_type = kwargs.pop('content_type', None) # type: Optional[str] + + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/batchGetResolvedUris') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + if content_type is not None: + header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str') + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="POST", + url=url, + headers=header_parameters, + **kwargs + ) + +# fmt: on +class DataVersionOperations(object): + """DataVersionOperations operations. + + You should not instantiate this class directly. Instead, you should create a Client instance that + instantiates it for you and attaches it as an attribute. + + :ivar models: Alias to model classes used in this operation group. + :type models: ~azure.mgmt.machinelearningservices.models + :param client: Client for service requests. + :param config: Configuration of service client. + :param serializer: An object model serializer. + :param deserializer: An object model deserializer. + """ + + models = _models + + def __init__(self, client, config, serializer, deserializer): + self._client = client + self._serialize = serializer + self._deserialize = deserializer + self._config = config + + @distributed_trace + def create( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + name, # type: str + body=None, # type: Optional["_models.DataVersion"] + **kwargs # type: Any + ): + # type: (...) -> "_models.DataVersionEntity" + """create. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param name: + :type name: str + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.DataVersion + :keyword callable cls: A custom type or function that will be passed the direct response + :return: DataVersionEntity, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.DataVersionEntity + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.DataVersionEntity"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'DataVersion') + else: + _json = None + + request = build_create_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + name=name, + content_type=content_type, + json=_json, + template_url=self.create.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('DataVersionEntity', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + create.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/{name}/versions'} # type: ignore + + + @distributed_trace + def list( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + name, # type: str + order_by=None, # type: Optional[str] + top=None, # type: Optional[int] + **kwargs # type: Any + ): + # type: (...) -> Iterable["_models.PaginatedDataVersionEntityList"] + """list. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param name: + :type name: str + :param order_by: + :type order_by: str + :param top: + :type top: int + :keyword callable cls: A custom type or function that will be passed the direct response + :return: An iterator like instance of either PaginatedDataVersionEntityList or the result of + cls(response) + :rtype: + ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.PaginatedDataVersionEntityList] + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.PaginatedDataVersionEntityList"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + def prepare_request(next_link=None): + if not next_link: + + request = build_list_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + name=name, + order_by=order_by, + top=top, + template_url=self.list.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + else: + + request = build_list_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + name=name, + order_by=order_by, + top=top, + template_url=next_link, + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + request.method = "GET" + return request + + def extract_data(pipeline_response): + deserialized = self._deserialize("PaginatedDataVersionEntityList", pipeline_response) + list_of_elem = deserialized.value + if cls: + list_of_elem = cls(list_of_elem) + return deserialized.next_link or None, iter(list_of_elem) + + def get_next(next_link=None): + request = prepare_request(next_link) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + return pipeline_response + + + return ItemPaged( + get_next, extract_data + ) + list.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/{name}/versions'} # type: ignore + + @distributed_trace + def get( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + name, # type: str + version, # type: str + **kwargs # type: Any + ): + # type: (...) -> "_models.DataVersionEntity" + """get. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param name: + :type name: str + :param version: + :type version: str + :keyword callable cls: A custom type or function that will be passed the direct response + :return: DataVersionEntity, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.DataVersionEntity + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.DataVersionEntity"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + + request = build_get_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + name=name, + version=version, + template_url=self.get.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('DataVersionEntity', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + get.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/{name}/versions/{version}'} # type: ignore + + + @distributed_trace + def modify( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + name, # type: str + version, # type: str + body=None, # type: Optional["_models.DataVersionMutable"] + **kwargs # type: Any + ): + # type: (...) -> "_models.DataVersionEntity" + """modify. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param name: + :type name: str + :param version: + :type version: str + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.DataVersionMutable + :keyword callable cls: A custom type or function that will be passed the direct response + :return: DataVersionEntity, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.DataVersionEntity + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.DataVersionEntity"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'DataVersionMutable') + else: + _json = None + + request = build_modify_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + name=name, + version=version, + content_type=content_type, + json=_json, + template_url=self.modify.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('DataVersionEntity', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + modify.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/{name}/versions/{version}'} # type: ignore + + + @distributed_trace + def delete( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + name, # type: str + version, # type: str + **kwargs # type: Any + ): + # type: (...) -> "_models.HttpResponseMessage" + """delete. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param name: + :type name: str + :param version: + :type version: str + :keyword callable cls: A custom type or function that will be passed the direct response + :return: HttpResponseMessage, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.HttpResponseMessage + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.HttpResponseMessage"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + + request = build_delete_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + name=name, + version=version, + template_url=self.delete.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('HttpResponseMessage', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + delete.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/{name}/versions/{version}'} # type: ignore + + + @distributed_trace + def exists( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + name, # type: str + version, # type: str + **kwargs # type: Any + ): + # type: (...) -> bool + """exists. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param name: + :type name: str + :param version: + :type version: str + :keyword callable cls: A custom type or function that will be passed the direct response + :return: bool, or the result of cls(response) + :rtype: bool + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType[bool] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + + request = build_exists_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + name=name, + version=version, + template_url=self.exists.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('bool', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + exists.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/{name}/versions/{version}/exists'} # type: ignore + + + @distributed_trace + def get_by_asset_id( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + body=None, # type: Optional["_models.AssetId"] + **kwargs # type: Any + ): + # type: (...) -> "_models.DataVersionEntity" + """get_by_asset_id. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.AssetId + :keyword callable cls: A custom type or function that will be passed the direct response + :return: DataVersionEntity, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.DataVersionEntity + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.DataVersionEntity"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'AssetId') + else: + _json = None + + request = build_get_by_asset_id_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + content_type=content_type, + json=_json, + template_url=self.get_by_asset_id.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('DataVersionEntity', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + get_by_asset_id.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/getByAssetId'} # type: ignore + + + @distributed_trace + def create_unregistered_input_data( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + body=None, # type: Optional["_models.CreateUnregisteredInputData"] + **kwargs # type: Any + ): + # type: (...) -> "_models.DataContainerEntity" + """create_unregistered_input_data. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.CreateUnregisteredInputData + :keyword callable cls: A custom type or function that will be passed the direct response + :return: DataContainerEntity, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.DataContainerEntity + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.DataContainerEntity"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'CreateUnregisteredInputData') + else: + _json = None + + request = build_create_unregistered_input_data_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + content_type=content_type, + json=_json, + template_url=self.create_unregistered_input_data.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('DataContainerEntity', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + create_unregistered_input_data.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/createUnregisteredInput'} # type: ignore + + + @distributed_trace + def create_unregistered_output_data( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + body=None, # type: Optional["_models.CreateUnregisteredOutputData"] + **kwargs # type: Any + ): + # type: (...) -> "_models.DataContainerEntity" + """create_unregistered_output_data. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.CreateUnregisteredOutputData + :keyword callable cls: A custom type or function that will be passed the direct response + :return: DataContainerEntity, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.DataContainerEntity + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.DataContainerEntity"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'CreateUnregisteredOutputData') + else: + _json = None + + request = build_create_unregistered_output_data_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + content_type=content_type, + json=_json, + template_url=self.create_unregistered_output_data.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('DataContainerEntity', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + create_unregistered_output_data.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/createUnregisteredOutput'} # type: ignore + + + @distributed_trace + def registered_existing_data( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + body=None, # type: Optional["_models.RegisterExistingData"] + **kwargs # type: Any + ): + # type: (...) -> "_models.DataContainerEntity" + """registered_existing_data. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.RegisterExistingData + :keyword callable cls: A custom type or function that will be passed the direct response + :return: DataContainerEntity, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.DataContainerEntity + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.DataContainerEntity"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'RegisterExistingData') + else: + _json = None + + request = build_registered_existing_data_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + content_type=content_type, + json=_json, + template_url=self.registered_existing_data.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('DataContainerEntity', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + registered_existing_data.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/registerExisting'} # type: ignore + + + @distributed_trace + def batch_get_resolved_uris( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + body=None, # type: Optional["_models.BatchGetResolvedURIs"] + **kwargs # type: Any + ): + # type: (...) -> "_models.BatchDataUriResponse" + """batch_get_resolved_uris. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.BatchGetResolvedURIs + :keyword callable cls: A custom type or function that will be passed the direct response + :return: BatchDataUriResponse, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.BatchDataUriResponse + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.BatchDataUriResponse"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'BatchGetResolvedURIs') + else: + _json = None + + request = build_batch_get_resolved_uris_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + content_type=content_type, + json=_json, + template_url=self.batch_get_resolved_uris.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('BatchDataUriResponse', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + batch_get_resolved_uris.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/batchGetResolvedUris'} # type: ignore + diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/_dataset_controller_v2_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/_dataset_controller_v2_operations.py new file mode 100644 index 00000000..05d64736 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/_dataset_controller_v2_operations.py @@ -0,0 +1,1300 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) AutoRest Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +import functools +from typing import TYPE_CHECKING +import warnings + +from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error +from azure.core.paging import ItemPaged +from azure.core.pipeline import PipelineResponse +from azure.core.pipeline.transport import HttpResponse +from azure.core.rest import HttpRequest +from azure.core.tracing.decorator import distributed_trace +from azure.mgmt.core.exceptions import ARMErrorFormat +from msrest import Serializer + +from .. import models as _models +from .._vendor import _convert_request, _format_url_section + +if TYPE_CHECKING: + # pylint: disable=unused-import,ungrouped-imports + from typing import Any, Callable, Dict, Generic, Iterable, List, Optional, TypeVar + T = TypeVar('T') + ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]] + +_SERIALIZER = Serializer() +_SERIALIZER.client_side_validation = False +# fmt: off + +def build_get_dataset_definition_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + dataset_id, # type: str + version, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}/definitions/{version}') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + "datasetId": _SERIALIZER.url("dataset_id", dataset_id, 'str'), + "version": _SERIALIZER.url("version", version, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="GET", + url=url, + headers=header_parameters, + **kwargs + ) + + +def build_get_all_dataset_definitions_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + dataset_id, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + continuation_token_parameter = kwargs.pop('continuation_token_parameter', None) # type: Optional[str] + page_size = kwargs.pop('page_size', None) # type: Optional[int] + + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}/definitions') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + "datasetId": _SERIALIZER.url("dataset_id", dataset_id, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct parameters + query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] + if continuation_token_parameter is not None: + query_parameters['continuationToken'] = _SERIALIZER.query("continuation_token_parameter", continuation_token_parameter, 'str') + if page_size is not None: + query_parameters['pageSize'] = _SERIALIZER.query("page_size", page_size, 'int') + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="GET", + url=url, + params=query_parameters, + headers=header_parameters, + **kwargs + ) + + +def build_update_definition_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + dataset_id, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + content_type = kwargs.pop('content_type', None) # type: Optional[str] + register_as_pending = kwargs.pop('register_as_pending', False) # type: Optional[bool] + force_update = kwargs.pop('force_update', False) # type: Optional[bool] + dataset_type = kwargs.pop('dataset_type', None) # type: Optional[str] + user_version_id = kwargs.pop('user_version_id', None) # type: Optional[str] + + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}/definitions') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + "datasetId": _SERIALIZER.url("dataset_id", dataset_id, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct parameters + query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] + if register_as_pending is not None: + query_parameters['registerAsPending'] = _SERIALIZER.query("register_as_pending", register_as_pending, 'bool') + if force_update is not None: + query_parameters['forceUpdate'] = _SERIALIZER.query("force_update", force_update, 'bool') + if dataset_type is not None: + query_parameters['datasetType'] = _SERIALIZER.query("dataset_type", dataset_type, 'str') + if user_version_id is not None: + query_parameters['userVersionId'] = _SERIALIZER.query("user_version_id", user_version_id, 'str') + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + if content_type is not None: + header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str') + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="POST", + url=url, + params=query_parameters, + headers=header_parameters, + **kwargs + ) + + +def build_get_all_dataset_versions_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + dataset_id, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + continuation_token_parameter = kwargs.pop('continuation_token_parameter', None) # type: Optional[str] + page_size = kwargs.pop('page_size', None) # type: Optional[int] + + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}/versions') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + "datasetId": _SERIALIZER.url("dataset_id", dataset_id, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct parameters + query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] + if continuation_token_parameter is not None: + query_parameters['continuationToken'] = _SERIALIZER.query("continuation_token_parameter", continuation_token_parameter, 'str') + if page_size is not None: + query_parameters['pageSize'] = _SERIALIZER.query("page_size", page_size, 'int') + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="GET", + url=url, + params=query_parameters, + headers=header_parameters, + **kwargs + ) + + +def build_get_dataset_by_name_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + dataset_name, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + version_id = kwargs.pop('version_id', None) # type: Optional[str] + include_latest_definition = kwargs.pop('include_latest_definition', True) # type: Optional[bool] + + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/query/name={datasetName}') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + "datasetName": _SERIALIZER.url("dataset_name", dataset_name, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct parameters + query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] + if version_id is not None: + query_parameters['versionId'] = _SERIALIZER.query("version_id", version_id, 'str') + if include_latest_definition is not None: + query_parameters['includeLatestDefinition'] = _SERIALIZER.query("include_latest_definition", include_latest_definition, 'bool') + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="GET", + url=url, + params=query_parameters, + headers=header_parameters, + **kwargs + ) + + +def build_list_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + dataset_names = kwargs.pop('dataset_names', None) # type: Optional[List[str]] + search_text = kwargs.pop('search_text', None) # type: Optional[str] + include_invisible = kwargs.pop('include_invisible', False) # type: Optional[bool] + status = kwargs.pop('status', None) # type: Optional[str] + continuation_token_parameter = kwargs.pop('continuation_token_parameter', None) # type: Optional[str] + page_size = kwargs.pop('page_size', None) # type: Optional[int] + include_latest_definition = kwargs.pop('include_latest_definition', False) # type: Optional[bool] + order_by = kwargs.pop('order_by', None) # type: Optional[str] + order_by_asc = kwargs.pop('order_by_asc', False) # type: Optional[bool] + dataset_types = kwargs.pop('dataset_types', None) # type: Optional[List[str]] + + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct parameters + query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] + if dataset_names is not None: + query_parameters['datasetNames'] = _SERIALIZER.query("dataset_names", dataset_names, '[str]') + if search_text is not None: + query_parameters['searchText'] = _SERIALIZER.query("search_text", search_text, 'str') + if include_invisible is not None: + query_parameters['includeInvisible'] = _SERIALIZER.query("include_invisible", include_invisible, 'bool') + if status is not None: + query_parameters['status'] = _SERIALIZER.query("status", status, 'str') + if continuation_token_parameter is not None: + query_parameters['continuationToken'] = _SERIALIZER.query("continuation_token_parameter", continuation_token_parameter, 'str') + if page_size is not None: + query_parameters['pageSize'] = _SERIALIZER.query("page_size", page_size, 'int') + if include_latest_definition is not None: + query_parameters['includeLatestDefinition'] = _SERIALIZER.query("include_latest_definition", include_latest_definition, 'bool') + if order_by is not None: + query_parameters['orderBy'] = _SERIALIZER.query("order_by", order_by, 'str') + if order_by_asc is not None: + query_parameters['orderByAsc'] = _SERIALIZER.query("order_by_asc", order_by_asc, 'bool') + if dataset_types is not None: + query_parameters['datasetTypes'] = _SERIALIZER.query("dataset_types", dataset_types, '[str]') + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="GET", + url=url, + params=query_parameters, + headers=header_parameters, + **kwargs + ) + + +def build_register_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + content_type = kwargs.pop('content_type', None) # type: Optional[str] + register_as_pending = kwargs.pop('register_as_pending', False) # type: Optional[bool] + if_exists_ok = kwargs.pop('if_exists_ok', True) # type: Optional[bool] + update_definition_if_exists = kwargs.pop('update_definition_if_exists', False) # type: Optional[bool] + with_data_hash = kwargs.pop('with_data_hash', False) # type: Optional[bool] + user_version_id = kwargs.pop('user_version_id', None) # type: Optional[str] + + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct parameters + query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] + if register_as_pending is not None: + query_parameters['registerAsPending'] = _SERIALIZER.query("register_as_pending", register_as_pending, 'bool') + if if_exists_ok is not None: + query_parameters['ifExistsOk'] = _SERIALIZER.query("if_exists_ok", if_exists_ok, 'bool') + if update_definition_if_exists is not None: + query_parameters['updateDefinitionIfExists'] = _SERIALIZER.query("update_definition_if_exists", update_definition_if_exists, 'bool') + if with_data_hash is not None: + query_parameters['withDataHash'] = _SERIALIZER.query("with_data_hash", with_data_hash, 'bool') + if user_version_id is not None: + query_parameters['userVersionId'] = _SERIALIZER.query("user_version_id", user_version_id, 'str') + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + if content_type is not None: + header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str') + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="POST", + url=url, + params=query_parameters, + headers=header_parameters, + **kwargs + ) + + +def build_delete_all_datasets_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="DELETE", + url=url, + headers=header_parameters, + **kwargs + ) + + +def build_update_dataset_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + dataset_id, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + content_type = kwargs.pop('content_type', None) # type: Optional[str] + force_update = kwargs.pop('force_update', False) # type: Optional[bool] + + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + "datasetId": _SERIALIZER.url("dataset_id", dataset_id, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct parameters + query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] + if force_update is not None: + query_parameters['forceUpdate'] = _SERIALIZER.query("force_update", force_update, 'bool') + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + if content_type is not None: + header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str') + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="PUT", + url=url, + params=query_parameters, + headers=header_parameters, + **kwargs + ) + + +def build_unregister_dataset_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + name, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{name}') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + "name": _SERIALIZER.url("name", name, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="DELETE", + url=url, + headers=header_parameters, + **kwargs + ) + +# fmt: on +class DatasetControllerV2Operations(object): + """DatasetControllerV2Operations operations. + + You should not instantiate this class directly. Instead, you should create a Client instance that + instantiates it for you and attaches it as an attribute. + + :ivar models: Alias to model classes used in this operation group. + :type models: ~azure.mgmt.machinelearningservices.models + :param client: Client for service requests. + :param config: Configuration of service client. + :param serializer: An object model serializer. + :param deserializer: An object model deserializer. + """ + + models = _models + + def __init__(self, client, config, serializer, deserializer): + self._client = client + self._serialize = serializer + self._deserialize = deserializer + self._config = config + + @distributed_trace + def get_dataset_definition( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + dataset_id, # type: str + version, # type: str + **kwargs # type: Any + ): + # type: (...) -> "_models.DatasetDefinition" + """Get a specific dataset definition. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param dataset_id: + :type dataset_id: str + :param version: + :type version: str + :keyword callable cls: A custom type or function that will be passed the direct response + :return: DatasetDefinition, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.DatasetDefinition + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.DatasetDefinition"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + + request = build_get_dataset_definition_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_id=dataset_id, + version=version, + template_url=self.get_dataset_definition.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('DatasetDefinition', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + get_dataset_definition.metadata = {'url': '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}/definitions/{version}'} # type: ignore + + + @distributed_trace + def get_all_dataset_definitions( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + dataset_id, # type: str + continuation_token_parameter=None, # type: Optional[str] + page_size=None, # type: Optional[int] + **kwargs # type: Any + ): + # type: (...) -> Iterable["_models.PaginatedDatasetDefinitionList"] + """Get all dataset definitions for a given dataset. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param dataset_id: + :type dataset_id: str + :param continuation_token_parameter: + :type continuation_token_parameter: str + :param page_size: + :type page_size: int + :keyword callable cls: A custom type or function that will be passed the direct response + :return: An iterator like instance of either PaginatedDatasetDefinitionList or the result of + cls(response) + :rtype: + ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.PaginatedDatasetDefinitionList] + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.PaginatedDatasetDefinitionList"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + def prepare_request(next_link=None): + if not next_link: + + request = build_get_all_dataset_definitions_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_id=dataset_id, + continuation_token_parameter=continuation_token_parameter, + page_size=page_size, + template_url=self.get_all_dataset_definitions.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + else: + + request = build_get_all_dataset_definitions_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_id=dataset_id, + continuation_token_parameter=continuation_token_parameter, + page_size=page_size, + template_url=next_link, + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + request.method = "GET" + return request + + def extract_data(pipeline_response): + deserialized = self._deserialize("PaginatedDatasetDefinitionList", pipeline_response) + list_of_elem = deserialized.value + if cls: + list_of_elem = cls(list_of_elem) + return deserialized.next_link or None, iter(list_of_elem) + + def get_next(next_link=None): + request = prepare_request(next_link) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + return pipeline_response + + + return ItemPaged( + get_next, extract_data + ) + get_all_dataset_definitions.metadata = {'url': '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}/definitions'} # type: ignore + + @distributed_trace + def update_definition( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + dataset_id, # type: str + register_as_pending=False, # type: Optional[bool] + force_update=False, # type: Optional[bool] + dataset_type=None, # type: Optional[str] + user_version_id=None, # type: Optional[str] + body=None, # type: Optional["_models.DatasetDefinition"] + **kwargs # type: Any + ): + # type: (...) -> "_models.Dataset" + """Update a dataset definition. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param dataset_id: + :type dataset_id: str + :param register_as_pending: + :type register_as_pending: bool + :param force_update: + :type force_update: bool + :param dataset_type: + :type dataset_type: str + :param user_version_id: + :type user_version_id: str + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.DatasetDefinition + :keyword callable cls: A custom type or function that will be passed the direct response + :return: Dataset, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.Dataset + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.Dataset"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'DatasetDefinition') + else: + _json = None + + request = build_update_definition_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_id=dataset_id, + content_type=content_type, + json=_json, + register_as_pending=register_as_pending, + force_update=force_update, + dataset_type=dataset_type, + user_version_id=user_version_id, + template_url=self.update_definition.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('Dataset', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + update_definition.metadata = {'url': '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}/definitions'} # type: ignore + + + @distributed_trace + def get_all_dataset_versions( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + dataset_id, # type: str + continuation_token_parameter=None, # type: Optional[str] + page_size=None, # type: Optional[int] + **kwargs # type: Any + ): + # type: (...) -> Iterable["_models.PaginatedStringList"] + """Get all dataset versions for a given dataset. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param dataset_id: + :type dataset_id: str + :param continuation_token_parameter: + :type continuation_token_parameter: str + :param page_size: + :type page_size: int + :keyword callable cls: A custom type or function that will be passed the direct response + :return: An iterator like instance of either PaginatedStringList or the result of cls(response) + :rtype: + ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.PaginatedStringList] + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.PaginatedStringList"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + def prepare_request(next_link=None): + if not next_link: + + request = build_get_all_dataset_versions_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_id=dataset_id, + continuation_token_parameter=continuation_token_parameter, + page_size=page_size, + template_url=self.get_all_dataset_versions.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + else: + + request = build_get_all_dataset_versions_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_id=dataset_id, + continuation_token_parameter=continuation_token_parameter, + page_size=page_size, + template_url=next_link, + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + request.method = "GET" + return request + + def extract_data(pipeline_response): + deserialized = self._deserialize("PaginatedStringList", pipeline_response) + list_of_elem = deserialized.value + if cls: + list_of_elem = cls(list_of_elem) + return deserialized.next_link or None, iter(list_of_elem) + + def get_next(next_link=None): + request = prepare_request(next_link) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + return pipeline_response + + + return ItemPaged( + get_next, extract_data + ) + get_all_dataset_versions.metadata = {'url': '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}/versions'} # type: ignore + + @distributed_trace + def get_dataset_by_name( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + dataset_name, # type: str + version_id=None, # type: Optional[str] + include_latest_definition=True, # type: Optional[bool] + **kwargs # type: Any + ): + # type: (...) -> "_models.Dataset" + """Get a dataset for a given dataset name. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param dataset_name: + :type dataset_name: str + :param version_id: + :type version_id: str + :param include_latest_definition: + :type include_latest_definition: bool + :keyword callable cls: A custom type or function that will be passed the direct response + :return: Dataset, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.Dataset + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.Dataset"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + + request = build_get_dataset_by_name_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_name=dataset_name, + version_id=version_id, + include_latest_definition=include_latest_definition, + template_url=self.get_dataset_by_name.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('Dataset', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + get_dataset_by_name.metadata = {'url': '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/query/name={datasetName}'} # type: ignore + + + @distributed_trace + def list( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + dataset_names=None, # type: Optional[List[str]] + search_text=None, # type: Optional[str] + include_invisible=False, # type: Optional[bool] + status=None, # type: Optional[str] + continuation_token_parameter=None, # type: Optional[str] + page_size=None, # type: Optional[int] + include_latest_definition=False, # type: Optional[bool] + order_by=None, # type: Optional[str] + order_by_asc=False, # type: Optional[bool] + dataset_types=None, # type: Optional[List[str]] + **kwargs # type: Any + ): + # type: (...) -> Iterable["_models.PaginatedDatasetList"] + """Get a list of datasets. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param dataset_names: + :type dataset_names: list[str] + :param search_text: + :type search_text: str + :param include_invisible: + :type include_invisible: bool + :param status: + :type status: str + :param continuation_token_parameter: + :type continuation_token_parameter: str + :param page_size: + :type page_size: int + :param include_latest_definition: + :type include_latest_definition: bool + :param order_by: + :type order_by: str + :param order_by_asc: + :type order_by_asc: bool + :param dataset_types: + :type dataset_types: list[str] + :keyword callable cls: A custom type or function that will be passed the direct response + :return: An iterator like instance of either PaginatedDatasetList or the result of + cls(response) + :rtype: + ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.PaginatedDatasetList] + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.PaginatedDatasetList"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + def prepare_request(next_link=None): + if not next_link: + + request = build_list_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_names=dataset_names, + search_text=search_text, + include_invisible=include_invisible, + status=status, + continuation_token_parameter=continuation_token_parameter, + page_size=page_size, + include_latest_definition=include_latest_definition, + order_by=order_by, + order_by_asc=order_by_asc, + dataset_types=dataset_types, + template_url=self.list.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + else: + + request = build_list_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_names=dataset_names, + search_text=search_text, + include_invisible=include_invisible, + status=status, + continuation_token_parameter=continuation_token_parameter, + page_size=page_size, + include_latest_definition=include_latest_definition, + order_by=order_by, + order_by_asc=order_by_asc, + dataset_types=dataset_types, + template_url=next_link, + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + request.method = "GET" + return request + + def extract_data(pipeline_response): + deserialized = self._deserialize("PaginatedDatasetList", pipeline_response) + list_of_elem = deserialized.value + if cls: + list_of_elem = cls(list_of_elem) + return deserialized.next_link or None, iter(list_of_elem) + + def get_next(next_link=None): + request = prepare_request(next_link) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + return pipeline_response + + + return ItemPaged( + get_next, extract_data + ) + list.metadata = {'url': '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets'} # type: ignore + + @distributed_trace + def register( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + register_as_pending=False, # type: Optional[bool] + if_exists_ok=True, # type: Optional[bool] + update_definition_if_exists=False, # type: Optional[bool] + with_data_hash=False, # type: Optional[bool] + user_version_id=None, # type: Optional[str] + body=None, # type: Optional["_models.Dataset"] + **kwargs # type: Any + ): + # type: (...) -> "_models.Dataset" + """Register new dataset. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param register_as_pending: + :type register_as_pending: bool + :param if_exists_ok: + :type if_exists_ok: bool + :param update_definition_if_exists: + :type update_definition_if_exists: bool + :param with_data_hash: + :type with_data_hash: bool + :param user_version_id: + :type user_version_id: str + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.Dataset + :keyword callable cls: A custom type or function that will be passed the direct response + :return: Dataset, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.Dataset + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.Dataset"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'Dataset') + else: + _json = None + + request = build_register_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + content_type=content_type, + json=_json, + register_as_pending=register_as_pending, + if_exists_ok=if_exists_ok, + update_definition_if_exists=update_definition_if_exists, + with_data_hash=with_data_hash, + user_version_id=user_version_id, + template_url=self.register.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('Dataset', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + register.metadata = {'url': '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets'} # type: ignore + + + @distributed_trace + def delete_all_datasets( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + **kwargs # type: Any + ): + # type: (...) -> None + """Unregister all datasets in the workspace. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :keyword callable cls: A custom type or function that will be passed the direct response + :return: None, or the result of cls(response) + :rtype: None + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType[None] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + + request = build_delete_all_datasets_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + template_url=self.delete_all_datasets.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in []: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + if cls: + return cls(pipeline_response, None, {}) + + delete_all_datasets.metadata = {'url': '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets'} # type: ignore + + + @distributed_trace + def update_dataset( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + dataset_id, # type: str + force_update=False, # type: Optional[bool] + body=None, # type: Optional["_models.Dataset"] + **kwargs # type: Any + ): + # type: (...) -> "_models.Dataset" + """Update a dataset. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param dataset_id: + :type dataset_id: str + :param force_update: + :type force_update: bool + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.Dataset + :keyword callable cls: A custom type or function that will be passed the direct response + :return: Dataset, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.Dataset + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.Dataset"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'Dataset') + else: + _json = None + + request = build_update_dataset_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_id=dataset_id, + content_type=content_type, + json=_json, + force_update=force_update, + template_url=self.update_dataset.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('Dataset', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + update_dataset.metadata = {'url': '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}'} # type: ignore + + + @distributed_trace + def unregister_dataset( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + name, # type: str + **kwargs # type: Any + ): + # type: (...) -> None + """Unregister a dataset. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param name: + :type name: str + :keyword callable cls: A custom type or function that will be passed the direct response + :return: None, or the result of cls(response) + :rtype: None + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType[None] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + + request = build_unregister_dataset_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + name=name, + template_url=self.unregister_dataset.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in []: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + if cls: + return cls(pipeline_response, None, {}) + + unregister_dataset.metadata = {'url': '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{name}'} # type: ignore + diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/_dataset_v2_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/_dataset_v2_operations.py new file mode 100644 index 00000000..7f686ab6 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/_dataset_v2_operations.py @@ -0,0 +1,905 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) AutoRest Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +import functools +from typing import TYPE_CHECKING +import warnings + +from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error +from azure.core.paging import ItemPaged +from azure.core.pipeline import PipelineResponse +from azure.core.pipeline.transport import HttpResponse +from azure.core.rest import HttpRequest +from azure.core.tracing.decorator import distributed_trace +from azure.mgmt.core.exceptions import ARMErrorFormat +from msrest import Serializer + +from .. import models as _models +from .._vendor import _convert_request, _format_url_section + +if TYPE_CHECKING: + # pylint: disable=unused-import,ungrouped-imports + from typing import Any, Callable, Dict, Generic, Iterable, List, Optional, TypeVar + T = TypeVar('T') + ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]] + +_SERIALIZER = Serializer() +_SERIALIZER.client_side_validation = False +# fmt: off + +def build_create_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + content_type = kwargs.pop('content_type', None) # type: Optional[str] + if_exists_update = kwargs.pop('if_exists_update', False) # type: Optional[bool] + + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct parameters + query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] + if if_exists_update is not None: + query_parameters['ifExistsUpdate'] = _SERIALIZER.query("if_exists_update", if_exists_update, 'bool') + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + if content_type is not None: + header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str') + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="POST", + url=url, + params=query_parameters, + headers=header_parameters, + **kwargs + ) + + +def build_delete_all_datasets_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="DELETE", + url=url, + headers=header_parameters, + **kwargs + ) + + +def build_list_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + names = kwargs.pop('names', None) # type: Optional[List[str]] + search_text = kwargs.pop('search_text', None) # type: Optional[str] + continuation_token_parameter = kwargs.pop('continuation_token_parameter', None) # type: Optional[str] + page_size = kwargs.pop('page_size', None) # type: Optional[int] + + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct parameters + query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] + if names is not None: + query_parameters['names'] = _SERIALIZER.query("names", names, '[str]') + if search_text is not None: + query_parameters['searchText'] = _SERIALIZER.query("search_text", search_text, 'str') + if continuation_token_parameter is not None: + query_parameters['continuationToken'] = _SERIALIZER.query("continuation_token_parameter", continuation_token_parameter, 'str') + if page_size is not None: + query_parameters['pageSize'] = _SERIALIZER.query("page_size", page_size, 'int') + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="GET", + url=url, + params=query_parameters, + headers=header_parameters, + **kwargs + ) + + +def build_delete_dataset_by_name_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + name, # type: str + version_id, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{name}/versions/{versionId}') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + "name": _SERIALIZER.url("name", name, 'str'), + "versionId": _SERIALIZER.url("version_id", version_id, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="DELETE", + url=url, + headers=header_parameters, + **kwargs + ) + + +def build_update_dataset_by_name_and_version_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + name, # type: str + version_id, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + content_type = kwargs.pop('content_type', None) # type: Optional[str] + + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{name}/versions/{versionId}') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + "name": _SERIALIZER.url("name", name, 'str'), + "versionId": _SERIALIZER.url("version_id", version_id, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + if content_type is not None: + header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str') + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="PUT", + url=url, + headers=header_parameters, + **kwargs + ) + + +def build_get_dataset_by_id_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + dataset_id, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + "datasetId": _SERIALIZER.url("dataset_id", dataset_id, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="GET", + url=url, + headers=header_parameters, + **kwargs + ) + + +def build_update_dataset_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + dataset_id, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + content_type = kwargs.pop('content_type', None) # type: Optional[str] + + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + "datasetId": _SERIALIZER.url("dataset_id", dataset_id, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + if content_type is not None: + header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str') + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="PUT", + url=url, + headers=header_parameters, + **kwargs + ) + + +def build_get_dataset_by_name_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + dataset_name, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + version_id = kwargs.pop('version_id', None) # type: Optional[str] + + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/query/name={datasetName}') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + "datasetName": _SERIALIZER.url("dataset_name", dataset_name, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct parameters + query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] + if version_id is not None: + query_parameters['versionId'] = _SERIALIZER.query("version_id", version_id, 'str') + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="GET", + url=url, + params=query_parameters, + headers=header_parameters, + **kwargs + ) + +# fmt: on +class DatasetV2Operations(object): + """DatasetV2Operations operations. + + You should not instantiate this class directly. Instead, you should create a Client instance that + instantiates it for you and attaches it as an attribute. + + :ivar models: Alias to model classes used in this operation group. + :type models: ~azure.mgmt.machinelearningservices.models + :param client: Client for service requests. + :param config: Configuration of service client. + :param serializer: An object model serializer. + :param deserializer: An object model deserializer. + """ + + models = _models + + def __init__(self, client, config, serializer, deserializer): + self._client = client + self._serialize = serializer + self._deserialize = deserializer + self._config = config + + @distributed_trace + def create( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + if_exists_update=False, # type: Optional[bool] + body=None, # type: Optional["_models.DatasetV2"] + **kwargs # type: Any + ): + # type: (...) -> "_models.DatasetV2" + """Create new dataset. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param if_exists_update: + :type if_exists_update: bool + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.DatasetV2 + :keyword callable cls: A custom type or function that will be passed the direct response + :return: DatasetV2, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.DatasetV2 + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.DatasetV2"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'DatasetV2') + else: + _json = None + + request = build_create_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + content_type=content_type, + json=_json, + if_exists_update=if_exists_update, + template_url=self.create.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('DatasetV2', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + create.metadata = {'url': '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets'} # type: ignore + + + @distributed_trace + def delete_all_datasets( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + **kwargs # type: Any + ): + # type: (...) -> None + """Delete all datasets in the workspace. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :keyword callable cls: A custom type or function that will be passed the direct response + :return: None, or the result of cls(response) + :rtype: None + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType[None] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + + request = build_delete_all_datasets_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + template_url=self.delete_all_datasets.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in []: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + if cls: + return cls(pipeline_response, None, {}) + + delete_all_datasets.metadata = {'url': '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets'} # type: ignore + + + @distributed_trace + def list( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + names=None, # type: Optional[List[str]] + search_text=None, # type: Optional[str] + continuation_token_parameter=None, # type: Optional[str] + page_size=None, # type: Optional[int] + **kwargs # type: Any + ): + # type: (...) -> Iterable["_models.PaginatedDatasetV2List"] + """Get a list of datasets. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param names: + :type names: list[str] + :param search_text: + :type search_text: str + :param continuation_token_parameter: + :type continuation_token_parameter: str + :param page_size: + :type page_size: int + :keyword callable cls: A custom type or function that will be passed the direct response + :return: An iterator like instance of either PaginatedDatasetV2List or the result of + cls(response) + :rtype: + ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.PaginatedDatasetV2List] + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.PaginatedDatasetV2List"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + def prepare_request(next_link=None): + if not next_link: + + request = build_list_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + names=names, + search_text=search_text, + continuation_token_parameter=continuation_token_parameter, + page_size=page_size, + template_url=self.list.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + else: + + request = build_list_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + names=names, + search_text=search_text, + continuation_token_parameter=continuation_token_parameter, + page_size=page_size, + template_url=next_link, + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + request.method = "GET" + return request + + def extract_data(pipeline_response): + deserialized = self._deserialize("PaginatedDatasetV2List", pipeline_response) + list_of_elem = deserialized.value + if cls: + list_of_elem = cls(list_of_elem) + return deserialized.next_link or None, iter(list_of_elem) + + def get_next(next_link=None): + request = prepare_request(next_link) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + return pipeline_response + + + return ItemPaged( + get_next, extract_data + ) + list.metadata = {'url': '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets'} # type: ignore + + @distributed_trace + def delete_dataset_by_name( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + name, # type: str + version_id, # type: str + **kwargs # type: Any + ): + # type: (...) -> None + """Delete a dataset. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param name: + :type name: str + :param version_id: + :type version_id: str + :keyword callable cls: A custom type or function that will be passed the direct response + :return: None, or the result of cls(response) + :rtype: None + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType[None] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + + request = build_delete_dataset_by_name_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + name=name, + version_id=version_id, + template_url=self.delete_dataset_by_name.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in []: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + if cls: + return cls(pipeline_response, None, {}) + + delete_dataset_by_name.metadata = {'url': '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{name}/versions/{versionId}'} # type: ignore + + + @distributed_trace + def update_dataset_by_name_and_version( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + name, # type: str + version_id, # type: str + body=None, # type: Optional["_models.DatasetV2"] + **kwargs # type: Any + ): + # type: (...) -> "_models.DatasetV2" + """Update a dataset by its name and version. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param name: + :type name: str + :param version_id: + :type version_id: str + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.DatasetV2 + :keyword callable cls: A custom type or function that will be passed the direct response + :return: DatasetV2, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.DatasetV2 + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.DatasetV2"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'DatasetV2') + else: + _json = None + + request = build_update_dataset_by_name_and_version_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + name=name, + version_id=version_id, + content_type=content_type, + json=_json, + template_url=self.update_dataset_by_name_and_version.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('DatasetV2', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + update_dataset_by_name_and_version.metadata = {'url': '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{name}/versions/{versionId}'} # type: ignore + + + @distributed_trace + def get_dataset_by_id( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + dataset_id, # type: str + **kwargs # type: Any + ): + # type: (...) -> "_models.DatasetV2" + """Get a dataset for a given dataset id. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param dataset_id: + :type dataset_id: str + :keyword callable cls: A custom type or function that will be passed the direct response + :return: DatasetV2, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.DatasetV2 + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.DatasetV2"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + + request = build_get_dataset_by_id_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_id=dataset_id, + template_url=self.get_dataset_by_id.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('DatasetV2', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + get_dataset_by_id.metadata = {'url': '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}'} # type: ignore + + + @distributed_trace + def update_dataset( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + dataset_id, # type: str + body=None, # type: Optional["_models.DatasetV2"] + **kwargs # type: Any + ): + # type: (...) -> "_models.DatasetV2" + """Update a dataset. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param dataset_id: + :type dataset_id: str + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.DatasetV2 + :keyword callable cls: A custom type or function that will be passed the direct response + :return: DatasetV2, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.DatasetV2 + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.DatasetV2"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'DatasetV2') + else: + _json = None + + request = build_update_dataset_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_id=dataset_id, + content_type=content_type, + json=_json, + template_url=self.update_dataset.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('DatasetV2', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + update_dataset.metadata = {'url': '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}'} # type: ignore + + + @distributed_trace + def get_dataset_by_name( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + dataset_name, # type: str + version_id=None, # type: Optional[str] + **kwargs # type: Any + ): + # type: (...) -> "_models.DatasetV2" + """Get a dataset for a given dataset name. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param dataset_name: + :type dataset_name: str + :param version_id: + :type version_id: str + :keyword callable cls: A custom type or function that will be passed the direct response + :return: DatasetV2, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.DatasetV2 + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.DatasetV2"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + + request = build_get_dataset_by_name_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_name=dataset_name, + version_id=version_id, + template_url=self.get_dataset_by_name.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('DatasetV2', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + get_dataset_by_name.metadata = {'url': '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/query/name={datasetName}'} # type: ignore + diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/_datasets_v1_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/_datasets_v1_operations.py new file mode 100644 index 00000000..df1b1710 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/_datasets_v1_operations.py @@ -0,0 +1,1300 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) AutoRest Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +import functools +from typing import TYPE_CHECKING +import warnings + +from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error +from azure.core.paging import ItemPaged +from azure.core.pipeline import PipelineResponse +from azure.core.pipeline.transport import HttpResponse +from azure.core.rest import HttpRequest +from azure.core.tracing.decorator import distributed_trace +from azure.mgmt.core.exceptions import ARMErrorFormat +from msrest import Serializer + +from .. import models as _models +from .._vendor import _convert_request, _format_url_section + +if TYPE_CHECKING: + # pylint: disable=unused-import,ungrouped-imports + from typing import Any, Callable, Dict, Generic, Iterable, List, Optional, TypeVar + T = TypeVar('T') + ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]] + +_SERIALIZER = Serializer() +_SERIALIZER.client_side_validation = False +# fmt: off + +def build_get_dataset_definition_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + dataset_id, # type: str + version, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}/definitions/{version}') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + "datasetId": _SERIALIZER.url("dataset_id", dataset_id, 'str'), + "version": _SERIALIZER.url("version", version, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="GET", + url=url, + headers=header_parameters, + **kwargs + ) + + +def build_get_all_dataset_definitions_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + dataset_id, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + continuation_token_parameter = kwargs.pop('continuation_token_parameter', None) # type: Optional[str] + page_size = kwargs.pop('page_size', None) # type: Optional[int] + + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}/definitions') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + "datasetId": _SERIALIZER.url("dataset_id", dataset_id, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct parameters + query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] + if continuation_token_parameter is not None: + query_parameters['continuationToken'] = _SERIALIZER.query("continuation_token_parameter", continuation_token_parameter, 'str') + if page_size is not None: + query_parameters['pageSize'] = _SERIALIZER.query("page_size", page_size, 'int') + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="GET", + url=url, + params=query_parameters, + headers=header_parameters, + **kwargs + ) + + +def build_update_definition_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + dataset_id, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + content_type = kwargs.pop('content_type', None) # type: Optional[str] + register_as_pending = kwargs.pop('register_as_pending', False) # type: Optional[bool] + force_update = kwargs.pop('force_update', False) # type: Optional[bool] + dataset_type = kwargs.pop('dataset_type', None) # type: Optional[str] + user_version_id = kwargs.pop('user_version_id', None) # type: Optional[str] + + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}/definitions') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + "datasetId": _SERIALIZER.url("dataset_id", dataset_id, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct parameters + query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] + if register_as_pending is not None: + query_parameters['registerAsPending'] = _SERIALIZER.query("register_as_pending", register_as_pending, 'bool') + if force_update is not None: + query_parameters['forceUpdate'] = _SERIALIZER.query("force_update", force_update, 'bool') + if dataset_type is not None: + query_parameters['datasetType'] = _SERIALIZER.query("dataset_type", dataset_type, 'str') + if user_version_id is not None: + query_parameters['userVersionId'] = _SERIALIZER.query("user_version_id", user_version_id, 'str') + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + if content_type is not None: + header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str') + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="POST", + url=url, + params=query_parameters, + headers=header_parameters, + **kwargs + ) + + +def build_get_all_dataset_versions_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + dataset_id, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + continuation_token_parameter = kwargs.pop('continuation_token_parameter', None) # type: Optional[str] + page_size = kwargs.pop('page_size', None) # type: Optional[int] + + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}/versions') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + "datasetId": _SERIALIZER.url("dataset_id", dataset_id, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct parameters + query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] + if continuation_token_parameter is not None: + query_parameters['continuationToken'] = _SERIALIZER.query("continuation_token_parameter", continuation_token_parameter, 'str') + if page_size is not None: + query_parameters['pageSize'] = _SERIALIZER.query("page_size", page_size, 'int') + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="GET", + url=url, + params=query_parameters, + headers=header_parameters, + **kwargs + ) + + +def build_get_dataset_by_name_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + dataset_name, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + version_id = kwargs.pop('version_id', None) # type: Optional[str] + include_latest_definition = kwargs.pop('include_latest_definition', True) # type: Optional[bool] + + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/query/name={datasetName}') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + "datasetName": _SERIALIZER.url("dataset_name", dataset_name, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct parameters + query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] + if version_id is not None: + query_parameters['versionId'] = _SERIALIZER.query("version_id", version_id, 'str') + if include_latest_definition is not None: + query_parameters['includeLatestDefinition'] = _SERIALIZER.query("include_latest_definition", include_latest_definition, 'bool') + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="GET", + url=url, + params=query_parameters, + headers=header_parameters, + **kwargs + ) + + +def build_list_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + dataset_names = kwargs.pop('dataset_names', None) # type: Optional[List[str]] + search_text = kwargs.pop('search_text', None) # type: Optional[str] + include_invisible = kwargs.pop('include_invisible', False) # type: Optional[bool] + status = kwargs.pop('status', None) # type: Optional[str] + continuation_token_parameter = kwargs.pop('continuation_token_parameter', None) # type: Optional[str] + page_size = kwargs.pop('page_size', None) # type: Optional[int] + include_latest_definition = kwargs.pop('include_latest_definition', False) # type: Optional[bool] + order_by = kwargs.pop('order_by', None) # type: Optional[str] + order_by_asc = kwargs.pop('order_by_asc', False) # type: Optional[bool] + dataset_types = kwargs.pop('dataset_types', None) # type: Optional[List[str]] + + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct parameters + query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] + if dataset_names is not None: + query_parameters['datasetNames'] = _SERIALIZER.query("dataset_names", dataset_names, '[str]') + if search_text is not None: + query_parameters['searchText'] = _SERIALIZER.query("search_text", search_text, 'str') + if include_invisible is not None: + query_parameters['includeInvisible'] = _SERIALIZER.query("include_invisible", include_invisible, 'bool') + if status is not None: + query_parameters['status'] = _SERIALIZER.query("status", status, 'str') + if continuation_token_parameter is not None: + query_parameters['continuationToken'] = _SERIALIZER.query("continuation_token_parameter", continuation_token_parameter, 'str') + if page_size is not None: + query_parameters['pageSize'] = _SERIALIZER.query("page_size", page_size, 'int') + if include_latest_definition is not None: + query_parameters['includeLatestDefinition'] = _SERIALIZER.query("include_latest_definition", include_latest_definition, 'bool') + if order_by is not None: + query_parameters['orderBy'] = _SERIALIZER.query("order_by", order_by, 'str') + if order_by_asc is not None: + query_parameters['orderByAsc'] = _SERIALIZER.query("order_by_asc", order_by_asc, 'bool') + if dataset_types is not None: + query_parameters['datasetTypes'] = _SERIALIZER.query("dataset_types", dataset_types, '[str]') + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="GET", + url=url, + params=query_parameters, + headers=header_parameters, + **kwargs + ) + + +def build_register_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + content_type = kwargs.pop('content_type', None) # type: Optional[str] + register_as_pending = kwargs.pop('register_as_pending', False) # type: Optional[bool] + if_exists_ok = kwargs.pop('if_exists_ok', True) # type: Optional[bool] + update_definition_if_exists = kwargs.pop('update_definition_if_exists', False) # type: Optional[bool] + with_data_hash = kwargs.pop('with_data_hash', False) # type: Optional[bool] + user_version_id = kwargs.pop('user_version_id', None) # type: Optional[str] + + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct parameters + query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] + if register_as_pending is not None: + query_parameters['registerAsPending'] = _SERIALIZER.query("register_as_pending", register_as_pending, 'bool') + if if_exists_ok is not None: + query_parameters['ifExistsOk'] = _SERIALIZER.query("if_exists_ok", if_exists_ok, 'bool') + if update_definition_if_exists is not None: + query_parameters['updateDefinitionIfExists'] = _SERIALIZER.query("update_definition_if_exists", update_definition_if_exists, 'bool') + if with_data_hash is not None: + query_parameters['withDataHash'] = _SERIALIZER.query("with_data_hash", with_data_hash, 'bool') + if user_version_id is not None: + query_parameters['userVersionId'] = _SERIALIZER.query("user_version_id", user_version_id, 'str') + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + if content_type is not None: + header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str') + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="POST", + url=url, + params=query_parameters, + headers=header_parameters, + **kwargs + ) + + +def build_delete_all_datasets_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="DELETE", + url=url, + headers=header_parameters, + **kwargs + ) + + +def build_update_dataset_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + dataset_id, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + content_type = kwargs.pop('content_type', None) # type: Optional[str] + force_update = kwargs.pop('force_update', False) # type: Optional[bool] + + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + "datasetId": _SERIALIZER.url("dataset_id", dataset_id, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct parameters + query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] + if force_update is not None: + query_parameters['forceUpdate'] = _SERIALIZER.query("force_update", force_update, 'bool') + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + if content_type is not None: + header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str') + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="PUT", + url=url, + params=query_parameters, + headers=header_parameters, + **kwargs + ) + + +def build_unregister_dataset_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + name, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{name}') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + "name": _SERIALIZER.url("name", name, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="DELETE", + url=url, + headers=header_parameters, + **kwargs + ) + +# fmt: on +class DatasetsV1Operations(object): + """DatasetsV1Operations operations. + + You should not instantiate this class directly. Instead, you should create a Client instance that + instantiates it for you and attaches it as an attribute. + + :ivar models: Alias to model classes used in this operation group. + :type models: ~azure.mgmt.machinelearningservices.models + :param client: Client for service requests. + :param config: Configuration of service client. + :param serializer: An object model serializer. + :param deserializer: An object model deserializer. + """ + + models = _models + + def __init__(self, client, config, serializer, deserializer): + self._client = client + self._serialize = serializer + self._deserialize = deserializer + self._config = config + + @distributed_trace + def get_dataset_definition( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + dataset_id, # type: str + version, # type: str + **kwargs # type: Any + ): + # type: (...) -> "_models.DatasetDefinition" + """Get a specific dataset definition. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param dataset_id: + :type dataset_id: str + :param version: + :type version: str + :keyword callable cls: A custom type or function that will be passed the direct response + :return: DatasetDefinition, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.DatasetDefinition + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.DatasetDefinition"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + + request = build_get_dataset_definition_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_id=dataset_id, + version=version, + template_url=self.get_dataset_definition.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('DatasetDefinition', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + get_dataset_definition.metadata = {'url': '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}/definitions/{version}'} # type: ignore + + + @distributed_trace + def get_all_dataset_definitions( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + dataset_id, # type: str + continuation_token_parameter=None, # type: Optional[str] + page_size=None, # type: Optional[int] + **kwargs # type: Any + ): + # type: (...) -> Iterable["_models.PaginatedDatasetDefinitionList"] + """Get all dataset definitions for a given dataset. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param dataset_id: + :type dataset_id: str + :param continuation_token_parameter: + :type continuation_token_parameter: str + :param page_size: + :type page_size: int + :keyword callable cls: A custom type or function that will be passed the direct response + :return: An iterator like instance of either PaginatedDatasetDefinitionList or the result of + cls(response) + :rtype: + ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.PaginatedDatasetDefinitionList] + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.PaginatedDatasetDefinitionList"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + def prepare_request(next_link=None): + if not next_link: + + request = build_get_all_dataset_definitions_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_id=dataset_id, + continuation_token_parameter=continuation_token_parameter, + page_size=page_size, + template_url=self.get_all_dataset_definitions.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + else: + + request = build_get_all_dataset_definitions_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_id=dataset_id, + continuation_token_parameter=continuation_token_parameter, + page_size=page_size, + template_url=next_link, + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + request.method = "GET" + return request + + def extract_data(pipeline_response): + deserialized = self._deserialize("PaginatedDatasetDefinitionList", pipeline_response) + list_of_elem = deserialized.value + if cls: + list_of_elem = cls(list_of_elem) + return deserialized.next_link or None, iter(list_of_elem) + + def get_next(next_link=None): + request = prepare_request(next_link) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + return pipeline_response + + + return ItemPaged( + get_next, extract_data + ) + get_all_dataset_definitions.metadata = {'url': '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}/definitions'} # type: ignore + + @distributed_trace + def update_definition( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + dataset_id, # type: str + register_as_pending=False, # type: Optional[bool] + force_update=False, # type: Optional[bool] + dataset_type=None, # type: Optional[str] + user_version_id=None, # type: Optional[str] + body=None, # type: Optional["_models.DatasetDefinition"] + **kwargs # type: Any + ): + # type: (...) -> "_models.Dataset" + """Update a dataset definition. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param dataset_id: + :type dataset_id: str + :param register_as_pending: + :type register_as_pending: bool + :param force_update: + :type force_update: bool + :param dataset_type: + :type dataset_type: str + :param user_version_id: + :type user_version_id: str + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.DatasetDefinition + :keyword callable cls: A custom type or function that will be passed the direct response + :return: Dataset, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.Dataset + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.Dataset"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'DatasetDefinition') + else: + _json = None + + request = build_update_definition_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_id=dataset_id, + content_type=content_type, + json=_json, + register_as_pending=register_as_pending, + force_update=force_update, + dataset_type=dataset_type, + user_version_id=user_version_id, + template_url=self.update_definition.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('Dataset', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + update_definition.metadata = {'url': '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}/definitions'} # type: ignore + + + @distributed_trace + def get_all_dataset_versions( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + dataset_id, # type: str + continuation_token_parameter=None, # type: Optional[str] + page_size=None, # type: Optional[int] + **kwargs # type: Any + ): + # type: (...) -> Iterable["_models.PaginatedStringList"] + """Get all dataset versions for a given dataset. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param dataset_id: + :type dataset_id: str + :param continuation_token_parameter: + :type continuation_token_parameter: str + :param page_size: + :type page_size: int + :keyword callable cls: A custom type or function that will be passed the direct response + :return: An iterator like instance of either PaginatedStringList or the result of cls(response) + :rtype: + ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.PaginatedStringList] + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.PaginatedStringList"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + def prepare_request(next_link=None): + if not next_link: + + request = build_get_all_dataset_versions_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_id=dataset_id, + continuation_token_parameter=continuation_token_parameter, + page_size=page_size, + template_url=self.get_all_dataset_versions.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + else: + + request = build_get_all_dataset_versions_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_id=dataset_id, + continuation_token_parameter=continuation_token_parameter, + page_size=page_size, + template_url=next_link, + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + request.method = "GET" + return request + + def extract_data(pipeline_response): + deserialized = self._deserialize("PaginatedStringList", pipeline_response) + list_of_elem = deserialized.value + if cls: + list_of_elem = cls(list_of_elem) + return deserialized.next_link or None, iter(list_of_elem) + + def get_next(next_link=None): + request = prepare_request(next_link) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + return pipeline_response + + + return ItemPaged( + get_next, extract_data + ) + get_all_dataset_versions.metadata = {'url': '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}/versions'} # type: ignore + + @distributed_trace + def get_dataset_by_name( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + dataset_name, # type: str + version_id=None, # type: Optional[str] + include_latest_definition=True, # type: Optional[bool] + **kwargs # type: Any + ): + # type: (...) -> "_models.Dataset" + """Get a dataset for a given dataset name. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param dataset_name: + :type dataset_name: str + :param version_id: + :type version_id: str + :param include_latest_definition: + :type include_latest_definition: bool + :keyword callable cls: A custom type or function that will be passed the direct response + :return: Dataset, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.Dataset + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.Dataset"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + + request = build_get_dataset_by_name_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_name=dataset_name, + version_id=version_id, + include_latest_definition=include_latest_definition, + template_url=self.get_dataset_by_name.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('Dataset', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + get_dataset_by_name.metadata = {'url': '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/query/name={datasetName}'} # type: ignore + + + @distributed_trace + def list( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + dataset_names=None, # type: Optional[List[str]] + search_text=None, # type: Optional[str] + include_invisible=False, # type: Optional[bool] + status=None, # type: Optional[str] + continuation_token_parameter=None, # type: Optional[str] + page_size=None, # type: Optional[int] + include_latest_definition=False, # type: Optional[bool] + order_by=None, # type: Optional[str] + order_by_asc=False, # type: Optional[bool] + dataset_types=None, # type: Optional[List[str]] + **kwargs # type: Any + ): + # type: (...) -> Iterable["_models.PaginatedDatasetList"] + """Get a list of datasets. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param dataset_names: + :type dataset_names: list[str] + :param search_text: + :type search_text: str + :param include_invisible: + :type include_invisible: bool + :param status: + :type status: str + :param continuation_token_parameter: + :type continuation_token_parameter: str + :param page_size: + :type page_size: int + :param include_latest_definition: + :type include_latest_definition: bool + :param order_by: + :type order_by: str + :param order_by_asc: + :type order_by_asc: bool + :param dataset_types: + :type dataset_types: list[str] + :keyword callable cls: A custom type or function that will be passed the direct response + :return: An iterator like instance of either PaginatedDatasetList or the result of + cls(response) + :rtype: + ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.PaginatedDatasetList] + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.PaginatedDatasetList"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + def prepare_request(next_link=None): + if not next_link: + + request = build_list_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_names=dataset_names, + search_text=search_text, + include_invisible=include_invisible, + status=status, + continuation_token_parameter=continuation_token_parameter, + page_size=page_size, + include_latest_definition=include_latest_definition, + order_by=order_by, + order_by_asc=order_by_asc, + dataset_types=dataset_types, + template_url=self.list.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + else: + + request = build_list_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_names=dataset_names, + search_text=search_text, + include_invisible=include_invisible, + status=status, + continuation_token_parameter=continuation_token_parameter, + page_size=page_size, + include_latest_definition=include_latest_definition, + order_by=order_by, + order_by_asc=order_by_asc, + dataset_types=dataset_types, + template_url=next_link, + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + request.method = "GET" + return request + + def extract_data(pipeline_response): + deserialized = self._deserialize("PaginatedDatasetList", pipeline_response) + list_of_elem = deserialized.value + if cls: + list_of_elem = cls(list_of_elem) + return deserialized.next_link or None, iter(list_of_elem) + + def get_next(next_link=None): + request = prepare_request(next_link) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + return pipeline_response + + + return ItemPaged( + get_next, extract_data + ) + list.metadata = {'url': '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets'} # type: ignore + + @distributed_trace + def register( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + register_as_pending=False, # type: Optional[bool] + if_exists_ok=True, # type: Optional[bool] + update_definition_if_exists=False, # type: Optional[bool] + with_data_hash=False, # type: Optional[bool] + user_version_id=None, # type: Optional[str] + body=None, # type: Optional["_models.Dataset"] + **kwargs # type: Any + ): + # type: (...) -> "_models.Dataset" + """Register new dataset. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param register_as_pending: + :type register_as_pending: bool + :param if_exists_ok: + :type if_exists_ok: bool + :param update_definition_if_exists: + :type update_definition_if_exists: bool + :param with_data_hash: + :type with_data_hash: bool + :param user_version_id: + :type user_version_id: str + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.Dataset + :keyword callable cls: A custom type or function that will be passed the direct response + :return: Dataset, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.Dataset + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.Dataset"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'Dataset') + else: + _json = None + + request = build_register_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + content_type=content_type, + json=_json, + register_as_pending=register_as_pending, + if_exists_ok=if_exists_ok, + update_definition_if_exists=update_definition_if_exists, + with_data_hash=with_data_hash, + user_version_id=user_version_id, + template_url=self.register.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('Dataset', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + register.metadata = {'url': '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets'} # type: ignore + + + @distributed_trace + def delete_all_datasets( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + **kwargs # type: Any + ): + # type: (...) -> None + """Unregister all datasets in the workspace. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :keyword callable cls: A custom type or function that will be passed the direct response + :return: None, or the result of cls(response) + :rtype: None + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType[None] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + + request = build_delete_all_datasets_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + template_url=self.delete_all_datasets.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in []: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + if cls: + return cls(pipeline_response, None, {}) + + delete_all_datasets.metadata = {'url': '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets'} # type: ignore + + + @distributed_trace + def update_dataset( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + dataset_id, # type: str + force_update=False, # type: Optional[bool] + body=None, # type: Optional["_models.Dataset"] + **kwargs # type: Any + ): + # type: (...) -> "_models.Dataset" + """Update a dataset. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param dataset_id: + :type dataset_id: str + :param force_update: + :type force_update: bool + :param body: + :type body: ~azure.mgmt.machinelearningservices.models.Dataset + :keyword callable cls: A custom type or function that will be passed the direct response + :return: Dataset, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.Dataset + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.Dataset"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] + + if body is not None: + _json = self._serialize.body(body, 'Dataset') + else: + _json = None + + request = build_update_dataset_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + dataset_id=dataset_id, + content_type=content_type, + json=_json, + force_update=force_update, + template_url=self.update_dataset.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('Dataset', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + update_dataset.metadata = {'url': '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}'} # type: ignore + + + @distributed_trace + def unregister_dataset( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + name, # type: str + **kwargs # type: Any + ): + # type: (...) -> None + """Unregister a dataset. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param name: + :type name: str + :keyword callable cls: A custom type or function that will be passed the direct response + :return: None, or the result of cls(response) + :rtype: None + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType[None] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + + request = build_unregister_dataset_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + name=name, + template_url=self.unregister_dataset.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in []: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + if cls: + return cls(pipeline_response, None, {}) + + unregister_dataset.metadata = {'url': '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{name}'} # type: ignore + diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/_delete_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/_delete_operations.py new file mode 100644 index 00000000..e1cd955c --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/_delete_operations.py @@ -0,0 +1,145 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) AutoRest Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +import functools +from typing import TYPE_CHECKING +import warnings + +from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error +from azure.core.pipeline import PipelineResponse +from azure.core.pipeline.transport import HttpResponse +from azure.core.rest import HttpRequest +from azure.core.tracing.decorator import distributed_trace +from azure.mgmt.core.exceptions import ARMErrorFormat +from msrest import Serializer + +from .. import models as _models +from .._vendor import _convert_request, _format_url_section + +if TYPE_CHECKING: + # pylint: disable=unused-import,ungrouped-imports + from typing import Any, Callable, Dict, Generic, Optional, TypeVar + T = TypeVar('T') + ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]] + +_SERIALIZER = Serializer() +_SERIALIZER.client_side_validation = False +# fmt: off + +def build_data_container_request( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + name, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datacontainer/{name}') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + "name": _SERIALIZER.url("name", name, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="DELETE", + url=url, + headers=header_parameters, + **kwargs + ) + +# fmt: on +class DeleteOperations(object): + """DeleteOperations operations. + + You should not instantiate this class directly. Instead, you should create a Client instance that + instantiates it for you and attaches it as an attribute. + + :ivar models: Alias to model classes used in this operation group. + :type models: ~azure.mgmt.machinelearningservices.models + :param client: Client for service requests. + :param config: Configuration of service client. + :param serializer: An object model serializer. + :param deserializer: An object model deserializer. + """ + + models = _models + + def __init__(self, client, config, serializer, deserializer): + self._client = client + self._serialize = serializer + self._deserialize = deserializer + self._config = config + + @distributed_trace + def data_container( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + name, # type: str + **kwargs # type: Any + ): + # type: (...) -> "_models.HttpResponseMessage" + """data_container. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param name: + :type name: str + :keyword callable cls: A custom type or function that will be passed the direct response + :return: HttpResponseMessage, or the result of cls(response) + :rtype: ~azure.mgmt.machinelearningservices.models.HttpResponseMessage + :raises: ~azure.core.exceptions.HttpResponseError + """ + cls = kwargs.pop('cls', None) # type: ClsType["_models.HttpResponseMessage"] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + + request = build_data_container_request( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + name=name, + template_url=self.data_container.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) + raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) + + deserialized = self._deserialize('HttpResponseMessage', pipeline_response) + + if cls: + return cls(pipeline_response, deserialized, {}) + + return deserialized + + data_container.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datacontainer/{name}'} # type: ignore + diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/_get_operation_status_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/_get_operation_status_operations.py new file mode 100644 index 00000000..085f9749 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/_get_operation_status_operations.py @@ -0,0 +1,212 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) AutoRest Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +import functools +from typing import TYPE_CHECKING +import warnings + +from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error +from azure.core.pipeline import PipelineResponse +from azure.core.pipeline.transport import HttpResponse +from azure.core.polling import LROPoller, NoPolling, PollingMethod +from azure.core.rest import HttpRequest +from azure.core.tracing.decorator import distributed_trace +from azure.mgmt.core.exceptions import ARMErrorFormat +from azure.mgmt.core.polling.arm_polling import ARMPolling +from msrest import Serializer + +from .. import models as _models +from .._vendor import _convert_request, _format_url_section + +if TYPE_CHECKING: + # pylint: disable=unused-import,ungrouped-imports + from typing import Any, Callable, Dict, Generic, Optional, TypeVar, Union + T = TypeVar('T') + ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]] + +_SERIALIZER = Serializer() +_SERIALIZER.client_side_validation = False +# fmt: off + +def build_get_dataset_operation_status_request_initial( + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + operation_id, # type: str + **kwargs # type: Any +): + # type: (...) -> HttpRequest + accept = "application/json" + # Construct URL + url = kwargs.pop("template_url", '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/operations/{operationId}') + path_format_arguments = { + "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), + "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), + "operationId": _SERIALIZER.url("operation_id", operation_id, 'str'), + } + + url = _format_url_section(url, **path_format_arguments) + + # Construct headers + header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] + header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') + + return HttpRequest( + method="GET", + url=url, + headers=header_parameters, + **kwargs + ) + +# fmt: on +class GetOperationStatusOperations(object): + """GetOperationStatusOperations operations. + + You should not instantiate this class directly. Instead, you should create a Client instance that + instantiates it for you and attaches it as an attribute. + + :ivar models: Alias to model classes used in this operation group. + :type models: ~azure.mgmt.machinelearningservices.models + :param client: Client for service requests. + :param config: Configuration of service client. + :param serializer: An object model serializer. + :param deserializer: An object model deserializer. + """ + + models = _models + + def __init__(self, client, config, serializer, deserializer): + self._client = client + self._serialize = serializer + self._deserialize = deserializer + self._config = config + + def _get_dataset_operation_status_initial( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + operation_id, # type: str + **kwargs # type: Any + ): + # type: (...) -> Optional["_models.LongRunningOperationResponse1LongRunningOperationResponseObject"] + cls = kwargs.pop('cls', None) # type: ClsType[Optional["_models.LongRunningOperationResponse1LongRunningOperationResponseObject"]] + error_map = { + 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError + } + error_map.update(kwargs.pop('error_map', {})) + + + request = build_get_dataset_operation_status_request_initial( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + operation_id=operation_id, + template_url=self._get_dataset_operation_status_initial.metadata['url'], + ) + request = _convert_request(request) + request.url = self._client.format_url(request.url) + + pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) + response = pipeline_response.http_response + + if response.status_code not in [200, 202]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response, error_format=ARMErrorFormat) + + deserialized = None + response_headers = {} + if response.status_code == 200: + deserialized = self._deserialize('LongRunningOperationResponse1LongRunningOperationResponseObject', pipeline_response) + + if response.status_code == 202: + response_headers['Location']=self._deserialize('str', response.headers.get('Location')) + + + if cls: + return cls(pipeline_response, deserialized, response_headers) + + return deserialized + + _get_dataset_operation_status_initial.metadata = {'url': '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/operations/{operationId}'} # type: ignore + + + @distributed_trace + def begin_get_dataset_operation_status( + self, + subscription_id, # type: str + resource_group_name, # type: str + workspace_name, # type: str + operation_id, # type: str + **kwargs # type: Any + ): + # type: (...) -> LROPoller["_models.LongRunningOperationResponse1LongRunningOperationResponseObject"] + """get_dataset_operation_status. + + :param subscription_id: The Azure Subscription ID. + :type subscription_id: str + :param resource_group_name: The Name of the resource group in which the workspace is located. + :type resource_group_name: str + :param workspace_name: The name of the workspace. + :type workspace_name: str + :param operation_id: + :type operation_id: str + :keyword callable cls: A custom type or function that will be passed the direct response + :keyword str continuation_token: A continuation token to restart a poller from a saved state. + :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this + operation to not poll, or pass in your own initialized polling object for a personal polling + strategy. + :paramtype polling: bool or ~azure.core.polling.PollingMethod + :keyword int polling_interval: Default waiting time between two polls for LRO operations if no + Retry-After header is present. + :return: An instance of LROPoller that returns either + LongRunningOperationResponse1LongRunningOperationResponseObject or the result of cls(response) + :rtype: + ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.LongRunningOperationResponse1LongRunningOperationResponseObject] + :raises: ~azure.core.exceptions.HttpResponseError + """ + polling = kwargs.pop('polling', True) # type: Union[bool, azure.core.polling.PollingMethod] + cls = kwargs.pop('cls', None) # type: ClsType["_models.LongRunningOperationResponse1LongRunningOperationResponseObject"] + lro_delay = kwargs.pop( + 'polling_interval', + self._config.polling_interval + ) + cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] + if cont_token is None: + raw_result = self._get_dataset_operation_status_initial( + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + operation_id=operation_id, + cls=lambda x,y,z: x, + **kwargs + ) + kwargs.pop('error_map', None) + + def get_long_running_output(pipeline_response): + response = pipeline_response.http_response + deserialized = self._deserialize('LongRunningOperationResponse1LongRunningOperationResponseObject', pipeline_response) + if cls: + return cls(pipeline_response, deserialized, {}) + return deserialized + + + if polling is True: polling_method = ARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, **kwargs) + elif polling is False: polling_method = NoPolling() + else: polling_method = polling + if cont_token: + return LROPoller.from_continuation_token( + polling_method=polling_method, + continuation_token=cont_token, + client=self._client, + deserialization_callback=get_long_running_output + ) + else: + return LROPoller(self._client, raw_result, get_long_running_output, polling_method) + + begin_get_dataset_operation_status.metadata = {'url': '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/operations/{operationId}'} # type: ignore diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/py.typed b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/py.typed new file mode 100644 index 00000000..e5aff4f8 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/py.typed @@ -0,0 +1 @@ +# Marker file for PEP 561. \ No newline at end of file |