two version of R2R are here HEAD master

1 files changed, 1681 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/google/genai/tunings.py b/.venv/lib/python3.12/site-packages/google/genai/tunings.py
new file mode 100644
index 00000000..a215a195
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/google/genai/tunings.py
@@ -0,0 +1,1681 @@
+# Copyright 2024 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# Code generated by the Google Gen AI SDK generator DO NOT EDIT.
+
+from typing import Optional, Union
+from urllib.parse import urlencode
+from . import _common
+from . import _transformers as t
+from . import types
+from ._api_client import ApiClient
+from ._common import get_value_by_path as getv
+from ._common import set_value_by_path as setv
+from .pagers import AsyncPager, Pager
+
+
+def _GetTuningJobConfig_to_mldev(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['http_options']) is not None:
+    setv(to_object, ['httpOptions'], getv(from_object, ['http_options']))
+
+  return to_object
+
+
+def _GetTuningJobConfig_to_vertex(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['http_options']) is not None:
+    setv(to_object, ['httpOptions'], getv(from_object, ['http_options']))
+
+  return to_object
+
+
+def _GetTuningJobParameters_to_mldev(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['name']) is not None:
+    setv(to_object, ['_url', 'name'], getv(from_object, ['name']))
+
+  if getv(from_object, ['config']) is not None:
+    setv(
+        to_object,
+        ['config'],
+        _GetTuningJobConfig_to_mldev(
+            api_client, getv(from_object, ['config']), to_object
+        ),
+    )
+
+  return to_object
+
+
+def _GetTuningJobParameters_to_vertex(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['name']) is not None:
+    setv(to_object, ['_url', 'name'], getv(from_object, ['name']))
+
+  if getv(from_object, ['config']) is not None:
+    setv(
+        to_object,
+        ['config'],
+        _GetTuningJobConfig_to_vertex(
+            api_client, getv(from_object, ['config']), to_object
+        ),
+    )
+
+  return to_object
+
+
+def _ListTuningJobsConfig_to_mldev(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['page_size']) is not None:
+    setv(
+        parent_object, ['_query', 'pageSize'], getv(from_object, ['page_size'])
+    )
+
+  if getv(from_object, ['page_token']) is not None:
+    setv(
+        parent_object,
+        ['_query', 'pageToken'],
+        getv(from_object, ['page_token']),
+    )
+
+  if getv(from_object, ['filter']) is not None:
+    setv(parent_object, ['_query', 'filter'], getv(from_object, ['filter']))
+
+  return to_object
+
+
+def _ListTuningJobsConfig_to_vertex(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['page_size']) is not None:
+    setv(
+        parent_object, ['_query', 'pageSize'], getv(from_object, ['page_size'])
+    )
+
+  if getv(from_object, ['page_token']) is not None:
+    setv(
+        parent_object,
+        ['_query', 'pageToken'],
+        getv(from_object, ['page_token']),
+    )
+
+  if getv(from_object, ['filter']) is not None:
+    setv(parent_object, ['_query', 'filter'], getv(from_object, ['filter']))
+
+  return to_object
+
+
+def _ListTuningJobsParameters_to_mldev(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['config']) is not None:
+    setv(
+        to_object,
+        ['config'],
+        _ListTuningJobsConfig_to_mldev(
+            api_client, getv(from_object, ['config']), to_object
+        ),
+    )
+
+  return to_object
+
+
+def _ListTuningJobsParameters_to_vertex(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['config']) is not None:
+    setv(
+        to_object,
+        ['config'],
+        _ListTuningJobsConfig_to_vertex(
+            api_client, getv(from_object, ['config']), to_object
+        ),
+    )
+
+  return to_object
+
+
+def _TuningExample_to_mldev(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['text_input']) is not None:
+    setv(to_object, ['textInput'], getv(from_object, ['text_input']))
+
+  if getv(from_object, ['output']) is not None:
+    setv(to_object, ['output'], getv(from_object, ['output']))
+
+  return to_object
+
+
+def _TuningExample_to_vertex(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['text_input']) is not None:
+    raise ValueError('text_input parameter is not supported in Vertex AI.')
+
+  if getv(from_object, ['output']) is not None:
+    raise ValueError('output parameter is not supported in Vertex AI.')
+
+  return to_object
+
+
+def _TuningDataset_to_mldev(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['gcs_uri']) is not None:
+    raise ValueError('gcs_uri parameter is not supported in Google AI.')
+
+  if getv(from_object, ['examples']) is not None:
+    setv(
+        to_object,
+        ['examples', 'examples'],
+        [
+            _TuningExample_to_mldev(api_client, item, to_object)
+            for item in getv(from_object, ['examples'])
+        ],
+    )
+
+  return to_object
+
+
+def _TuningDataset_to_vertex(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['gcs_uri']) is not None:
+    setv(
+        parent_object,
+        ['supervisedTuningSpec', 'trainingDatasetUri'],
+        getv(from_object, ['gcs_uri']),
+    )
+
+  if getv(from_object, ['examples']) is not None:
+    raise ValueError('examples parameter is not supported in Vertex AI.')
+
+  return to_object
+
+
+def _TuningValidationDataset_to_mldev(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['gcs_uri']) is not None:
+    raise ValueError('gcs_uri parameter is not supported in Google AI.')
+
+  return to_object
+
+
+def _TuningValidationDataset_to_vertex(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['gcs_uri']) is not None:
+    setv(to_object, ['validationDatasetUri'], getv(from_object, ['gcs_uri']))
+
+  return to_object
+
+
+def _CreateTuningJobConfig_to_mldev(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['http_options']) is not None:
+    setv(to_object, ['httpOptions'], getv(from_object, ['http_options']))
+
+  if getv(from_object, ['validation_dataset']) is not None:
+    raise ValueError(
+        'validation_dataset parameter is not supported in Google AI.'
+    )
+
+  if getv(from_object, ['tuned_model_display_name']) is not None:
+    setv(
+        parent_object,
+        ['displayName'],
+        getv(from_object, ['tuned_model_display_name']),
+    )
+
+  if getv(from_object, ['description']) is not None:
+    raise ValueError('description parameter is not supported in Google AI.')
+
+  if getv(from_object, ['epoch_count']) is not None:
+    setv(
+        parent_object,
+        ['tuningTask', 'hyperparameters', 'epochCount'],
+        getv(from_object, ['epoch_count']),
+    )
+
+  if getv(from_object, ['learning_rate_multiplier']) is not None:
+    setv(
+        to_object,
+        ['tuningTask', 'hyperparameters', 'learningRateMultiplier'],
+        getv(from_object, ['learning_rate_multiplier']),
+    )
+
+  if getv(from_object, ['adapter_size']) is not None:
+    raise ValueError('adapter_size parameter is not supported in Google AI.')
+
+  if getv(from_object, ['batch_size']) is not None:
+    setv(
+        parent_object,
+        ['tuningTask', 'hyperparameters', 'batchSize'],
+        getv(from_object, ['batch_size']),
+    )
+
+  if getv(from_object, ['learning_rate']) is not None:
+    setv(
+        parent_object,
+        ['tuningTask', 'hyperparameters', 'learningRate'],
+        getv(from_object, ['learning_rate']),
+    )
+
+  return to_object
+
+
+def _CreateTuningJobConfig_to_vertex(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['http_options']) is not None:
+    setv(to_object, ['httpOptions'], getv(from_object, ['http_options']))
+
+  if getv(from_object, ['validation_dataset']) is not None:
+    setv(
+        parent_object,
+        ['supervisedTuningSpec'],
+        _TuningValidationDataset_to_vertex(
+            api_client, getv(from_object, ['validation_dataset']), to_object
+        ),
+    )
+
+  if getv(from_object, ['tuned_model_display_name']) is not None:
+    setv(
+        parent_object,
+        ['tunedModelDisplayName'],
+        getv(from_object, ['tuned_model_display_name']),
+    )
+
+  if getv(from_object, ['description']) is not None:
+    setv(parent_object, ['description'], getv(from_object, ['description']))
+
+  if getv(from_object, ['epoch_count']) is not None:
+    setv(
+        parent_object,
+        ['supervisedTuningSpec', 'hyperParameters', 'epochCount'],
+        getv(from_object, ['epoch_count']),
+    )
+
+  if getv(from_object, ['learning_rate_multiplier']) is not None:
+    setv(
+        to_object,
+        ['supervisedTuningSpec', 'hyperParameters', 'learningRateMultiplier'],
+        getv(from_object, ['learning_rate_multiplier']),
+    )
+
+  if getv(from_object, ['adapter_size']) is not None:
+    setv(
+        parent_object,
+        ['supervisedTuningSpec', 'hyperParameters', 'adapterSize'],
+        getv(from_object, ['adapter_size']),
+    )
+
+  if getv(from_object, ['batch_size']) is not None:
+    raise ValueError('batch_size parameter is not supported in Vertex AI.')
+
+  if getv(from_object, ['learning_rate']) is not None:
+    raise ValueError('learning_rate parameter is not supported in Vertex AI.')
+
+  return to_object
+
+
+def _CreateTuningJobParameters_to_mldev(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['base_model']) is not None:
+    setv(to_object, ['baseModel'], getv(from_object, ['base_model']))
+
+  if getv(from_object, ['training_dataset']) is not None:
+    setv(
+        to_object,
+        ['tuningTask', 'trainingData'],
+        _TuningDataset_to_mldev(
+            api_client, getv(from_object, ['training_dataset']), to_object
+        ),
+    )
+
+  if getv(from_object, ['config']) is not None:
+    setv(
+        to_object,
+        ['config'],
+        _CreateTuningJobConfig_to_mldev(
+            api_client, getv(from_object, ['config']), to_object
+        ),
+    )
+
+  return to_object
+
+
+def _CreateTuningJobParameters_to_vertex(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['base_model']) is not None:
+    setv(to_object, ['baseModel'], getv(from_object, ['base_model']))
+
+  if getv(from_object, ['training_dataset']) is not None:
+    setv(
+        to_object,
+        ['supervisedTuningSpec', 'trainingDatasetUri'],
+        _TuningDataset_to_vertex(
+            api_client, getv(from_object, ['training_dataset']), to_object
+        ),
+    )
+
+  if getv(from_object, ['config']) is not None:
+    setv(
+        to_object,
+        ['config'],
+        _CreateTuningJobConfig_to_vertex(
+            api_client, getv(from_object, ['config']), to_object
+        ),
+    )
+
+  return to_object
+
+
+def _DistillationDataset_to_mldev(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['gcs_uri']) is not None:
+    raise ValueError('gcs_uri parameter is not supported in Google AI.')
+
+  return to_object
+
+
+def _DistillationDataset_to_vertex(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['gcs_uri']) is not None:
+    setv(
+        parent_object,
+        ['distillationSpec', 'trainingDatasetUri'],
+        getv(from_object, ['gcs_uri']),
+    )
+
+  return to_object
+
+
+def _DistillationValidationDataset_to_mldev(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['gcs_uri']) is not None:
+    raise ValueError('gcs_uri parameter is not supported in Google AI.')
+
+  return to_object
+
+
+def _DistillationValidationDataset_to_vertex(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['gcs_uri']) is not None:
+    setv(to_object, ['validationDatasetUri'], getv(from_object, ['gcs_uri']))
+
+  return to_object
+
+
+def _CreateDistillationJobConfig_to_mldev(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['http_options']) is not None:
+    setv(to_object, ['httpOptions'], getv(from_object, ['http_options']))
+
+  if getv(from_object, ['validation_dataset']) is not None:
+    raise ValueError(
+        'validation_dataset parameter is not supported in Google AI.'
+    )
+
+  if getv(from_object, ['tuned_model_display_name']) is not None:
+    setv(
+        parent_object,
+        ['displayName'],
+        getv(from_object, ['tuned_model_display_name']),
+    )
+
+  if getv(from_object, ['epoch_count']) is not None:
+    setv(
+        parent_object,
+        ['tuningTask', 'hyperparameters', 'epochCount'],
+        getv(from_object, ['epoch_count']),
+    )
+
+  if getv(from_object, ['learning_rate_multiplier']) is not None:
+    setv(
+        parent_object,
+        ['tuningTask', 'hyperparameters', 'learningRateMultiplier'],
+        getv(from_object, ['learning_rate_multiplier']),
+    )
+
+  if getv(from_object, ['adapter_size']) is not None:
+    raise ValueError('adapter_size parameter is not supported in Google AI.')
+
+  if getv(from_object, ['pipeline_root_directory']) is not None:
+    raise ValueError(
+        'pipeline_root_directory parameter is not supported in Google AI.'
+    )
+
+  return to_object
+
+
+def _CreateDistillationJobConfig_to_vertex(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['http_options']) is not None:
+    setv(to_object, ['httpOptions'], getv(from_object, ['http_options']))
+
+  if getv(from_object, ['validation_dataset']) is not None:
+    setv(
+        parent_object,
+        ['distillationSpec'],
+        _DistillationValidationDataset_to_vertex(
+            api_client, getv(from_object, ['validation_dataset']), to_object
+        ),
+    )
+
+  if getv(from_object, ['tuned_model_display_name']) is not None:
+    setv(
+        parent_object,
+        ['tunedModelDisplayName'],
+        getv(from_object, ['tuned_model_display_name']),
+    )
+
+  if getv(from_object, ['epoch_count']) is not None:
+    setv(
+        parent_object,
+        ['distillationSpec', 'hyperParameters', 'epochCount'],
+        getv(from_object, ['epoch_count']),
+    )
+
+  if getv(from_object, ['learning_rate_multiplier']) is not None:
+    setv(
+        parent_object,
+        ['distillationSpec', 'hyperParameters', 'learningRateMultiplier'],
+        getv(from_object, ['learning_rate_multiplier']),
+    )
+
+  if getv(from_object, ['adapter_size']) is not None:
+    setv(
+        parent_object,
+        ['distillationSpec', 'hyperParameters', 'adapterSize'],
+        getv(from_object, ['adapter_size']),
+    )
+
+  if getv(from_object, ['pipeline_root_directory']) is not None:
+    setv(
+        parent_object,
+        ['distillationSpec', 'pipelineRootDirectory'],
+        getv(from_object, ['pipeline_root_directory']),
+    )
+
+  return to_object
+
+
+def _CreateDistillationJobParameters_to_mldev(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['student_model']) is not None:
+    raise ValueError('student_model parameter is not supported in Google AI.')
+
+  if getv(from_object, ['teacher_model']) is not None:
+    raise ValueError('teacher_model parameter is not supported in Google AI.')
+
+  if getv(from_object, ['training_dataset']) is not None:
+    setv(
+        to_object,
+        ['tuningTask', 'trainingData'],
+        _DistillationDataset_to_mldev(
+            api_client, getv(from_object, ['training_dataset']), to_object
+        ),
+    )
+
+  if getv(from_object, ['config']) is not None:
+    setv(
+        to_object,
+        ['config'],
+        _CreateDistillationJobConfig_to_mldev(
+            api_client, getv(from_object, ['config']), to_object
+        ),
+    )
+
+  return to_object
+
+
+def _CreateDistillationJobParameters_to_vertex(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['student_model']) is not None:
+    setv(
+        to_object,
+        ['distillationSpec', 'studentModel'],
+        getv(from_object, ['student_model']),
+    )
+
+  if getv(from_object, ['teacher_model']) is not None:
+    setv(
+        to_object,
+        ['distillationSpec', 'baseTeacherModel'],
+        getv(from_object, ['teacher_model']),
+    )
+
+  if getv(from_object, ['training_dataset']) is not None:
+    setv(
+        to_object,
+        ['distillationSpec', 'trainingDatasetUri'],
+        _DistillationDataset_to_vertex(
+            api_client, getv(from_object, ['training_dataset']), to_object
+        ),
+    )
+
+  if getv(from_object, ['config']) is not None:
+    setv(
+        to_object,
+        ['config'],
+        _CreateDistillationJobConfig_to_vertex(
+            api_client, getv(from_object, ['config']), to_object
+        ),
+    )
+
+  return to_object
+
+
+def _TunedModel_from_mldev(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['name']) is not None:
+    setv(to_object, ['model'], getv(from_object, ['name']))
+
+  if getv(from_object, ['name']) is not None:
+    setv(to_object, ['endpoint'], getv(from_object, ['name']))
+
+  return to_object
+
+
+def _TunedModel_from_vertex(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['model']) is not None:
+    setv(to_object, ['model'], getv(from_object, ['model']))
+
+  if getv(from_object, ['endpoint']) is not None:
+    setv(to_object, ['endpoint'], getv(from_object, ['endpoint']))
+
+  return to_object
+
+
+def _TuningJob_from_mldev(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['name']) is not None:
+    setv(to_object, ['name'], getv(from_object, ['name']))
+
+  if getv(from_object, ['state']) is not None:
+    setv(
+        to_object,
+        ['state'],
+        t.t_tuning_job_status(api_client, getv(from_object, ['state'])),
+    )
+
+  if getv(from_object, ['createTime']) is not None:
+    setv(to_object, ['create_time'], getv(from_object, ['createTime']))
+
+  if getv(from_object, ['tuningTask', 'startTime']) is not None:
+    setv(
+        to_object,
+        ['start_time'],
+        getv(from_object, ['tuningTask', 'startTime']),
+    )
+
+  if getv(from_object, ['tuningTask', 'completeTime']) is not None:
+    setv(
+        to_object,
+        ['end_time'],
+        getv(from_object, ['tuningTask', 'completeTime']),
+    )
+
+  if getv(from_object, ['updateTime']) is not None:
+    setv(to_object, ['update_time'], getv(from_object, ['updateTime']))
+
+  if getv(from_object, ['description']) is not None:
+    setv(to_object, ['description'], getv(from_object, ['description']))
+
+  if getv(from_object, ['baseModel']) is not None:
+    setv(to_object, ['base_model'], getv(from_object, ['baseModel']))
+
+  if getv(from_object, ['_self']) is not None:
+    setv(
+        to_object,
+        ['tuned_model'],
+        _TunedModel_from_mldev(
+            api_client, getv(from_object, ['_self']), to_object
+        ),
+    )
+
+  if getv(from_object, ['experiment']) is not None:
+    setv(to_object, ['experiment'], getv(from_object, ['experiment']))
+
+  if getv(from_object, ['labels']) is not None:
+    setv(to_object, ['labels'], getv(from_object, ['labels']))
+
+  if getv(from_object, ['tunedModelDisplayName']) is not None:
+    setv(
+        to_object,
+        ['tuned_model_display_name'],
+        getv(from_object, ['tunedModelDisplayName']),
+    )
+
+  return to_object
+
+
+def _TuningJob_from_vertex(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['name']) is not None:
+    setv(to_object, ['name'], getv(from_object, ['name']))
+
+  if getv(from_object, ['state']) is not None:
+    setv(
+        to_object,
+        ['state'],
+        t.t_tuning_job_status(api_client, getv(from_object, ['state'])),
+    )
+
+  if getv(from_object, ['createTime']) is not None:
+    setv(to_object, ['create_time'], getv(from_object, ['createTime']))
+
+  if getv(from_object, ['startTime']) is not None:
+    setv(to_object, ['start_time'], getv(from_object, ['startTime']))
+
+  if getv(from_object, ['endTime']) is not None:
+    setv(to_object, ['end_time'], getv(from_object, ['endTime']))
+
+  if getv(from_object, ['updateTime']) is not None:
+    setv(to_object, ['update_time'], getv(from_object, ['updateTime']))
+
+  if getv(from_object, ['error']) is not None:
+    setv(to_object, ['error'], getv(from_object, ['error']))
+
+  if getv(from_object, ['description']) is not None:
+    setv(to_object, ['description'], getv(from_object, ['description']))
+
+  if getv(from_object, ['baseModel']) is not None:
+    setv(to_object, ['base_model'], getv(from_object, ['baseModel']))
+
+  if getv(from_object, ['tunedModel']) is not None:
+    setv(
+        to_object,
+        ['tuned_model'],
+        _TunedModel_from_vertex(
+            api_client, getv(from_object, ['tunedModel']), to_object
+        ),
+    )
+
+  if getv(from_object, ['supervisedTuningSpec']) is not None:
+    setv(
+        to_object,
+        ['supervised_tuning_spec'],
+        getv(from_object, ['supervisedTuningSpec']),
+    )
+
+  if getv(from_object, ['tuningDataStats']) is not None:
+    setv(
+        to_object, ['tuning_data_stats'], getv(from_object, ['tuningDataStats'])
+    )
+
+  if getv(from_object, ['encryptionSpec']) is not None:
+    setv(to_object, ['encryption_spec'], getv(from_object, ['encryptionSpec']))
+
+  if getv(from_object, ['distillationSpec']) is not None:
+    setv(
+        to_object,
+        ['distillation_spec'],
+        getv(from_object, ['distillationSpec']),
+    )
+
+  if getv(from_object, ['partnerModelTuningSpec']) is not None:
+    setv(
+        to_object,
+        ['partner_model_tuning_spec'],
+        getv(from_object, ['partnerModelTuningSpec']),
+    )
+
+  if getv(from_object, ['pipelineJob']) is not None:
+    setv(to_object, ['pipeline_job'], getv(from_object, ['pipelineJob']))
+
+  if getv(from_object, ['experiment']) is not None:
+    setv(to_object, ['experiment'], getv(from_object, ['experiment']))
+
+  if getv(from_object, ['labels']) is not None:
+    setv(to_object, ['labels'], getv(from_object, ['labels']))
+
+  if getv(from_object, ['tunedModelDisplayName']) is not None:
+    setv(
+        to_object,
+        ['tuned_model_display_name'],
+        getv(from_object, ['tunedModelDisplayName']),
+    )
+
+  return to_object
+
+
+def _ListTuningJobsResponse_from_mldev(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['nextPageToken']) is not None:
+    setv(to_object, ['next_page_token'], getv(from_object, ['nextPageToken']))
+
+  if getv(from_object, ['tunedModels']) is not None:
+    setv(
+        to_object,
+        ['tuning_jobs'],
+        [
+            _TuningJob_from_mldev(api_client, item, to_object)
+            for item in getv(from_object, ['tunedModels'])
+        ],
+    )
+
+  return to_object
+
+
+def _ListTuningJobsResponse_from_vertex(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['nextPageToken']) is not None:
+    setv(to_object, ['next_page_token'], getv(from_object, ['nextPageToken']))
+
+  if getv(from_object, ['tuningJobs']) is not None:
+    setv(
+        to_object,
+        ['tuning_jobs'],
+        [
+            _TuningJob_from_vertex(api_client, item, to_object)
+            for item in getv(from_object, ['tuningJobs'])
+        ],
+    )
+
+  return to_object
+
+
+def _TuningJobOrOperation_from_mldev(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['_self']) is not None:
+    setv(
+        to_object,
+        ['tuning_job'],
+        _TuningJob_from_mldev(
+            api_client,
+            t.t_resolve_operation(api_client, getv(from_object, ['_self'])),
+            to_object,
+        ),
+    )
+
+  return to_object
+
+
+def _TuningJobOrOperation_from_vertex(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['_self']) is not None:
+    setv(
+        to_object,
+        ['tuning_job'],
+        _TuningJob_from_vertex(
+            api_client,
+            t.t_resolve_operation(api_client, getv(from_object, ['_self'])),
+            to_object,
+        ),
+    )
+
+  return to_object
+
+
+class Tunings(_common.BaseModule):
+
+  def _get(
+      self,
+      *,
+      name: str,
+      config: Optional[types.GetTuningJobConfigOrDict] = None,
+  ) -> types.TuningJob:
+    """Gets a TuningJob.
+
+    Args:
+      name: The resource name of the tuning job.
+
+    Returns:
+      A TuningJob object.
+    """
+
+    parameter_model = types._GetTuningJobParameters(
+        name=name,
+        config=config,
+    )
+
+    if self._api_client.vertexai:
+      request_dict = _GetTuningJobParameters_to_vertex(
+          self._api_client, parameter_model
+      )
+      path = '{name}'.format_map(request_dict.get('_url'))
+    else:
+      request_dict = _GetTuningJobParameters_to_mldev(
+          self._api_client, parameter_model
+      )
+      path = '{name}'.format_map(request_dict.get('_url'))
+    query_params = request_dict.get('_query')
+    if query_params:
+      path = f'{path}?{urlencode(query_params)}'
+    # TODO: remove the hack that pops config.
+    config = request_dict.pop('config', None)
+    http_options = config.pop('httpOptions', None) if config else None
+    request_dict = _common.convert_to_dict(request_dict)
+    request_dict = _common.encode_unserializable_types(request_dict)
+
+    response_dict = self._api_client.request(
+        'get', path, request_dict, http_options
+    )
+
+    if self._api_client.vertexai:
+      response_dict = _TuningJob_from_vertex(self._api_client, response_dict)
+    else:
+      response_dict = _TuningJob_from_mldev(self._api_client, response_dict)
+
+    return_value = types.TuningJob._from_response(
+        response_dict, parameter_model
+    )
+    self._api_client._verify_response(return_value)
+    return return_value
+
+  def _list(
+      self, *, config: Optional[types.ListTuningJobsConfigOrDict] = None
+  ) -> types.ListTuningJobsResponse:
+    """Lists tuning jobs.
+
+    Args:
+      config: The configuration for the list request.
+
+    Returns:
+      A list of tuning jobs.
+    """
+
+    parameter_model = types._ListTuningJobsParameters(
+        config=config,
+    )
+
+    if self._api_client.vertexai:
+      request_dict = _ListTuningJobsParameters_to_vertex(
+          self._api_client, parameter_model
+      )
+      path = 'tuningJobs'.format_map(request_dict.get('_url'))
+    else:
+      request_dict = _ListTuningJobsParameters_to_mldev(
+          self._api_client, parameter_model
+      )
+      path = 'tunedModels'.format_map(request_dict.get('_url'))
+    query_params = request_dict.get('_query')
+    if query_params:
+      path = f'{path}?{urlencode(query_params)}'
+    # TODO: remove the hack that pops config.
+    config = request_dict.pop('config', None)
+    http_options = config.pop('httpOptions', None) if config else None
+    request_dict = _common.convert_to_dict(request_dict)
+    request_dict = _common.encode_unserializable_types(request_dict)
+
+    response_dict = self._api_client.request(
+        'get', path, request_dict, http_options
+    )
+
+    if self._api_client.vertexai:
+      response_dict = _ListTuningJobsResponse_from_vertex(
+          self._api_client, response_dict
+      )
+    else:
+      response_dict = _ListTuningJobsResponse_from_mldev(
+          self._api_client, response_dict
+      )
+
+    return_value = types.ListTuningJobsResponse._from_response(
+        response_dict, parameter_model
+    )
+    self._api_client._verify_response(return_value)
+    return return_value
+
+  def _tune(
+      self,
+      *,
+      base_model: str,
+      training_dataset: types.TuningDatasetOrDict,
+      config: Optional[types.CreateTuningJobConfigOrDict] = None,
+  ) -> types.TuningJobOrOperation:
+    """Creates a supervised fine-tuning job.
+
+    Args:
+      base_model: The name of the model to tune.
+      training_dataset: The training dataset to use.
+      config: The configuration to use for the tuning job.
+
+    Returns:
+      A TuningJob object.
+    """
+
+    parameter_model = types._CreateTuningJobParameters(
+        base_model=base_model,
+        training_dataset=training_dataset,
+        config=config,
+    )
+
+    if self._api_client.vertexai:
+      request_dict = _CreateTuningJobParameters_to_vertex(
+          self._api_client, parameter_model
+      )
+      path = 'tuningJobs'.format_map(request_dict.get('_url'))
+    else:
+      request_dict = _CreateTuningJobParameters_to_mldev(
+          self._api_client, parameter_model
+      )
+      path = 'tunedModels'.format_map(request_dict.get('_url'))
+    query_params = request_dict.get('_query')
+    if query_params:
+      path = f'{path}?{urlencode(query_params)}'
+    # TODO: remove the hack that pops config.
+    config = request_dict.pop('config', None)
+    http_options = config.pop('httpOptions', None) if config else None
+    request_dict = _common.convert_to_dict(request_dict)
+    request_dict = _common.encode_unserializable_types(request_dict)
+
+    response_dict = self._api_client.request(
+        'post', path, request_dict, http_options
+    )
+
+    if self._api_client.vertexai:
+      response_dict = _TuningJobOrOperation_from_vertex(
+          self._api_client, response_dict
+      )
+    else:
+      response_dict = _TuningJobOrOperation_from_mldev(
+          self._api_client, response_dict
+      )
+
+    return_value = types.TuningJobOrOperation._from_response(
+        response_dict, parameter_model
+    ).tuning_job
+    self._api_client._verify_response(return_value)
+    return return_value
+
+  def distill(
+      self,
+      *,
+      student_model: str,
+      teacher_model: str,
+      training_dataset: types.DistillationDatasetOrDict,
+      config: Optional[types.CreateDistillationJobConfigOrDict] = None,
+  ) -> types.TuningJob:
+    """Creates a distillation job.
+
+    Args:
+      student_model: The name of the model to tune.
+      teacher_model: The name of the model to distill from.
+      training_dataset: The training dataset to use.
+      config: The configuration to use for the distillation job.
+
+    Returns:
+      A TuningJob object.
+    """
+
+    parameter_model = types._CreateDistillationJobParameters(
+        student_model=student_model,
+        teacher_model=teacher_model,
+        training_dataset=training_dataset,
+        config=config,
+    )
+
+    if not self._api_client.vertexai:
+      raise ValueError('This method is only supported in the Vertex AI client.')
+    else:
+      request_dict = _CreateDistillationJobParameters_to_vertex(
+          self._api_client, parameter_model
+      )
+      path = 'tuningJobs'.format_map(request_dict.get('_url'))
+
+    query_params = request_dict.get('_query')
+    if query_params:
+      path = f'{path}?{urlencode(query_params)}'
+    # TODO: remove the hack that pops config.
+    config = request_dict.pop('config', None)
+    http_options = config.pop('httpOptions', None) if config else None
+    request_dict = _common.convert_to_dict(request_dict)
+    request_dict = _common.encode_unserializable_types(request_dict)
+
+    response_dict = self._api_client.request(
+        'post', path, request_dict, http_options
+    )
+
+    if self._api_client.vertexai:
+      response_dict = _TuningJob_from_vertex(self._api_client, response_dict)
+    else:
+      response_dict = _TuningJob_from_mldev(self._api_client, response_dict)
+
+    return_value = types.TuningJob._from_response(
+        response_dict, parameter_model
+    )
+    self._api_client._verify_response(return_value)
+    return return_value
+
+  def list(
+      self, *, config: Optional[types.ListTuningJobsConfigOrDict] = None
+  ) -> Pager[types.TuningJob]:
+    return Pager(
+        'tuning_jobs',
+        self._list,
+        self._list(config=config),
+        config,
+    )
+
+  def get(
+      self,
+      *,
+      name: str,
+      config: Optional[types.GetTuningJobConfigOrDict] = None,
+  ) -> types.TuningJob:
+    job = self._get(name=name, config=config)
+    if job.experiment and self._api_client.vertexai:
+      _IpythonUtils.display_experiment_button(
+          experiment=job.experiment,
+          project=self._api_client.project,
+      )
+    return job
+
+  def tune(
+      self,
+      *,
+      base_model: str,
+      training_dataset: types.TuningDatasetOrDict,
+      config: Optional[types.CreateTuningJobConfigOrDict] = None,
+  ) -> types.TuningJobOrOperation:
+    result = self._tune(
+        base_model=base_model,
+        training_dataset=training_dataset,
+        config=config,
+    )
+    if result.name and self._api_client.vertexai:
+      _IpythonUtils.display_model_tuning_button(tuning_job_resource=result.name)
+    return result
+
+
+class AsyncTunings(_common.BaseModule):
+
+  async def _get(
+      self,
+      *,
+      name: str,
+      config: Optional[types.GetTuningJobConfigOrDict] = None,
+  ) -> types.TuningJob:
+    """Gets a TuningJob.
+
+    Args:
+      name: The resource name of the tuning job.
+
+    Returns:
+      A TuningJob object.
+    """
+
+    parameter_model = types._GetTuningJobParameters(
+        name=name,
+        config=config,
+    )
+
+    if self._api_client.vertexai:
+      request_dict = _GetTuningJobParameters_to_vertex(
+          self._api_client, parameter_model
+      )
+      path = '{name}'.format_map(request_dict.get('_url'))
+    else:
+      request_dict = _GetTuningJobParameters_to_mldev(
+          self._api_client, parameter_model
+      )
+      path = '{name}'.format_map(request_dict.get('_url'))
+    query_params = request_dict.get('_query')
+    if query_params:
+      path = f'{path}?{urlencode(query_params)}'
+    # TODO: remove the hack that pops config.
+    config = request_dict.pop('config', None)
+    http_options = config.pop('httpOptions', None) if config else None
+    request_dict = _common.convert_to_dict(request_dict)
+    request_dict = _common.encode_unserializable_types(request_dict)
+
+    response_dict = await self._api_client.async_request(
+        'get', path, request_dict, http_options
+    )
+
+    if self._api_client.vertexai:
+      response_dict = _TuningJob_from_vertex(self._api_client, response_dict)
+    else:
+      response_dict = _TuningJob_from_mldev(self._api_client, response_dict)
+
+    return_value = types.TuningJob._from_response(
+        response_dict, parameter_model
+    )
+    self._api_client._verify_response(return_value)
+    return return_value
+
+  async def _list(
+      self, *, config: Optional[types.ListTuningJobsConfigOrDict] = None
+  ) -> types.ListTuningJobsResponse:
+    """Lists tuning jobs.
+
+    Args:
+      config: The configuration for the list request.
+
+    Returns:
+      A list of tuning jobs.
+    """
+
+    parameter_model = types._ListTuningJobsParameters(
+        config=config,
+    )
+
+    if self._api_client.vertexai:
+      request_dict = _ListTuningJobsParameters_to_vertex(
+          self._api_client, parameter_model
+      )
+      path = 'tuningJobs'.format_map(request_dict.get('_url'))
+    else:
+      request_dict = _ListTuningJobsParameters_to_mldev(
+          self._api_client, parameter_model
+      )
+      path = 'tunedModels'.format_map(request_dict.get('_url'))
+    query_params = request_dict.get('_query')
+    if query_params:
+      path = f'{path}?{urlencode(query_params)}'
+    # TODO: remove the hack that pops config.
+    config = request_dict.pop('config', None)
+    http_options = config.pop('httpOptions', None) if config else None
+    request_dict = _common.convert_to_dict(request_dict)
+    request_dict = _common.encode_unserializable_types(request_dict)
+
+    response_dict = await self._api_client.async_request(
+        'get', path, request_dict, http_options
+    )
+
+    if self._api_client.vertexai:
+      response_dict = _ListTuningJobsResponse_from_vertex(
+          self._api_client, response_dict
+      )
+    else:
+      response_dict = _ListTuningJobsResponse_from_mldev(
+          self._api_client, response_dict
+      )
+
+    return_value = types.ListTuningJobsResponse._from_response(
+        response_dict, parameter_model
+    )
+    self._api_client._verify_response(return_value)
+    return return_value
+
+  async def _tune(
+      self,
+      *,
+      base_model: str,
+      training_dataset: types.TuningDatasetOrDict,
+      config: Optional[types.CreateTuningJobConfigOrDict] = None,
+  ) -> types.TuningJobOrOperation:
+    """Creates a supervised fine-tuning job.
+
+    Args:
+      base_model: The name of the model to tune.
+      training_dataset: The training dataset to use.
+      config: The configuration to use for the tuning job.
+
+    Returns:
+      A TuningJob object.
+    """
+
+    parameter_model = types._CreateTuningJobParameters(
+        base_model=base_model,
+        training_dataset=training_dataset,
+        config=config,
+    )
+
+    if self._api_client.vertexai:
+      request_dict = _CreateTuningJobParameters_to_vertex(
+          self._api_client, parameter_model
+      )
+      path = 'tuningJobs'.format_map(request_dict.get('_url'))
+    else:
+      request_dict = _CreateTuningJobParameters_to_mldev(
+          self._api_client, parameter_model
+      )
+      path = 'tunedModels'.format_map(request_dict.get('_url'))
+    query_params = request_dict.get('_query')
+    if query_params:
+      path = f'{path}?{urlencode(query_params)}'
+    # TODO: remove the hack that pops config.
+    config = request_dict.pop('config', None)
+    http_options = config.pop('httpOptions', None) if config else None
+    request_dict = _common.convert_to_dict(request_dict)
+    request_dict = _common.encode_unserializable_types(request_dict)
+
+    response_dict = await self._api_client.async_request(
+        'post', path, request_dict, http_options
+    )
+
+    if self._api_client.vertexai:
+      response_dict = _TuningJobOrOperation_from_vertex(
+          self._api_client, response_dict
+      )
+    else:
+      response_dict = _TuningJobOrOperation_from_mldev(
+          self._api_client, response_dict
+      )
+
+    return_value = types.TuningJobOrOperation._from_response(
+        response_dict, parameter_model
+    ).tuning_job
+    self._api_client._verify_response(return_value)
+    return return_value
+
+  async def distill(
+      self,
+      *,
+      student_model: str,
+      teacher_model: str,
+      training_dataset: types.DistillationDatasetOrDict,
+      config: Optional[types.CreateDistillationJobConfigOrDict] = None,
+  ) -> types.TuningJob:
+    """Creates a distillation job.
+
+    Args:
+      student_model: The name of the model to tune.
+      teacher_model: The name of the model to distill from.
+      training_dataset: The training dataset to use.
+      config: The configuration to use for the distillation job.
+
+    Returns:
+      A TuningJob object.
+    """
+
+    parameter_model = types._CreateDistillationJobParameters(
+        student_model=student_model,
+        teacher_model=teacher_model,
+        training_dataset=training_dataset,
+        config=config,
+    )
+
+    if not self._api_client.vertexai:
+      raise ValueError('This method is only supported in the Vertex AI client.')
+    else:
+      request_dict = _CreateDistillationJobParameters_to_vertex(
+          self._api_client, parameter_model
+      )
+      path = 'tuningJobs'.format_map(request_dict.get('_url'))
+
+    query_params = request_dict.get('_query')
+    if query_params:
+      path = f'{path}?{urlencode(query_params)}'
+    # TODO: remove the hack that pops config.
+    config = request_dict.pop('config', None)
+    http_options = config.pop('httpOptions', None) if config else None
+    request_dict = _common.convert_to_dict(request_dict)
+    request_dict = _common.encode_unserializable_types(request_dict)
+
+    response_dict = await self._api_client.async_request(
+        'post', path, request_dict, http_options
+    )
+
+    if self._api_client.vertexai:
+      response_dict = _TuningJob_from_vertex(self._api_client, response_dict)
+    else:
+      response_dict = _TuningJob_from_mldev(self._api_client, response_dict)
+
+    return_value = types.TuningJob._from_response(
+        response_dict, parameter_model
+    )
+    self._api_client._verify_response(return_value)
+    return return_value
+
+  async def list(
+      self, *, config: Optional[types.ListTuningJobsConfigOrDict] = None
+  ) -> AsyncPager[types.TuningJob]:
+    return AsyncPager(
+        'tuning_jobs',
+        self._list,
+        await self._list(config=config),
+        config,
+    )
+
+  async def get(
+      self,
+      *,
+      name: str,
+      config: Optional[types.GetTuningJobConfigOrDict] = None,
+  ) -> types.TuningJob:
+    job = await self._get(name=name, config=config)
+    if job.experiment and self._api_client.vertexai:
+      _IpythonUtils.display_experiment_button(
+          experiment=job.experiment,
+          project=self._api_client.project,
+      )
+    return job
+
+  async def tune(
+      self,
+      *,
+      base_model: str,
+      training_dataset: types.TuningDatasetOrDict,
+      config: Optional[types.CreateTuningJobConfigOrDict] = None,
+  ) -> types.TuningJobOrOperation:
+    result = await self._tune(
+        base_model=base_model,
+        training_dataset=training_dataset,
+        config=config,
+    )
+    if result.name and self._api_client.vertexai:
+      _IpythonUtils.display_model_tuning_button(tuning_job_resource=result.name)
+    return result
+
+
+class _IpythonUtils:
+  """Temporary class to hold the IPython related functions."""
+
+  displayed_experiments = set()
+
+  @staticmethod
+  def _get_ipython_shell_name() -> str:
+    import sys
+
+    if 'IPython' in sys.modules:
+      from IPython import get_ipython
+
+      return get_ipython().__class__.__name__
+    return ''
+
+  @staticmethod
+  def is_ipython_available() -> bool:
+    return bool(_IpythonUtils._get_ipython_shell_name())
+
+  @staticmethod
+  def _get_styles() -> None:
+    """Returns the HTML style markup to support custom buttons."""
+    return """
+    <link rel="stylesheet" href="https://fonts.googleapis.com/icon?family=Material+Icons">
+    <style>
+      .view-vertex-resource,
+      .view-vertex-resource:hover,
+      .view-vertex-resource:visited {
+        position: relative;
+        display: inline-flex;
+        flex-direction: row;
+        height: 32px;
+        padding: 0 12px;
+          margin: 4px 18px;
+        gap: 4px;
+        border-radius: 4px;
+
+        align-items: center;
+        justify-content: center;
+        background-color: rgb(255, 255, 255);
+        color: rgb(51, 103, 214);
+
+        font-family: Roboto,"Helvetica Neue",sans-serif;
+        font-size: 13px;
+        font-weight: 500;
+        text-transform: uppercase;
+        text-decoration: none !important;
+
+        transition: box-shadow 280ms cubic-bezier(0.4, 0, 0.2, 1) 0s;
+        box-shadow: 0px 3px 1px -2px rgba(0,0,0,0.2), 0px 2px 2px 0px rgba(0,0,0,0.14), 0px 1px 5px 0px rgba(0,0,0,0.12);
+      }
+      .view-vertex-resource:active {
+        box-shadow: 0px 5px 5px -3px rgba(0,0,0,0.2),0px 8px 10px 1px rgba(0,0,0,0.14),0px 3px 14px 2px rgba(0,0,0,0.12);
+      }
+      .view-vertex-resource:active .view-vertex-ripple::before {
+        position: absolute;
+        top: 0;
+        bottom: 0;
+        left: 0;
+        right: 0;
+        border-radius: 4px;
+        pointer-events: none;
+
+        content: '';
+        background-color: rgb(51, 103, 214);
+        opacity: 0.12;
+      }
+      .view-vertex-icon {
+        font-size: 18px;
+      }
+    </style>
+  """
+
+  @staticmethod
+  def _parse_resource_name(marker: str, resource_parts: list[str]) -> str:
+    """Returns the part after the marker text part."""
+    for i in range(len(resource_parts)):
+      if resource_parts[i] == marker and i + 1 < len(resource_parts):
+        return resource_parts[i + 1]
+    return ''
+
+  @staticmethod
+  def _display_link(
+      text: str, url: str, icon: Optional[str] = 'open_in_new'
+  ) -> None:
+    """Creates and displays the link to open the Vertex resource.
+
+    Args:
+      text: The text displayed on the clickable button.
+      url: The url that the button will lead to. Only cloud console URIs are
+        allowed.
+      icon: The icon name on the button (from material-icons library)
+    """
+    CLOUD_UI_URL = 'https://console.cloud.google.com'  # pylint: disable=invalid-name
+    if not url.startswith(CLOUD_UI_URL):
+      raise ValueError(f'Only urls starting with {CLOUD_UI_URL} are allowed.')
+
+    import uuid
+
+    button_id = f'view-vertex-resource-{str(uuid.uuid4())}'
+
+    # Add the markup for the CSS and link component
+    html = f"""
+        {_IpythonUtils._get_styles()}
+        <a class="view-vertex-resource" id="{button_id}" href="#view-{button_id}">
+          <span class="material-icons view-vertex-icon">{icon}</span>
+          <span>{text}</span>
+        </a>
+        """
+
+    # Add the click handler for the link
+    html += f"""
+        <script>
+          (function () {{
+            const link = document.getElementById('{button_id}');
+            link.addEventListener('click', (e) => {{
+              if (window.google?.colab?.openUrl) {{
+                window.google.colab.openUrl('{url}');
+              }} else {{
+                window.open('{url}', '_blank');
+              }}
+              e.stopPropagation();
+              e.preventDefault();
+            }});
+          }})();
+        </script>
+    """
+
+    from IPython.core.display import display
+    from IPython.display import HTML
+
+    display(HTML(html))
+
+  @staticmethod
+  def display_experiment_button(experiment: str, project: str) -> None:
+    """Function to generate a link bound to the Vertex experiment.
+
+    Args:
+      experiment: The Vertex experiment name. Example format:
+        projects/{project_id}/locations/{location}/metadataStores/default/contexts/{experiment_name}
+      project: The project (alphanumeric) name.
+    """
+    if (
+        not _IpythonUtils.is_ipython_available()
+        or experiment in _IpythonUtils.displayed_experiments
+    ):
+      return
+    # Experiment gives the numeric id, but we need the alphanumeric project
+    # name. So we get the project from the api client object as an argument.
+    resource_parts = experiment.split('/')
+    location = resource_parts[3]
+    experiment_name = resource_parts[-1]
+
+    uri = (
+        'https://console.cloud.google.com/vertex-ai/experiments/locations/'
+        + f'{location}/experiments/{experiment_name}/'
+        + f'runs?project={project}'
+    )
+    _IpythonUtils._display_link('View Experiment', uri, 'science')
+
+    # Avoid repeatedly showing the button
+    _IpythonUtils.displayed_experiments.add(experiment)
+
+  @staticmethod
+  def display_model_tuning_button(tuning_job_resource: str) -> None:
+    """Function to generate a link bound to the Vertex model tuning job.
+
+    Args:
+      tuning_job_resource: The Vertex tuning job name. Example format:
+        projects/{project_id}/locations/{location}/tuningJobs/{tuning_job_id}
+    """
+    if not _IpythonUtils.is_ipython_available():
+      return
+
+    resource_parts = tuning_job_resource.split('/')
+    project = resource_parts[1]
+    location = resource_parts[3]
+    tuning_job_id = resource_parts[-1]
+
+    uri = (
+        'https://console.cloud.google.com/vertex-ai/generative/language/'
+        + f'locations/{location}/tuning/tuningJob/{tuning_job_id}'
+        + f'?project={project}'
+    )
+    _IpythonUtils._display_link('View Tuning Job', uri, 'tune')