two version of R2R are here HEAD master

1 files changed, 1293 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/google/genai/batches.py b/.venv/lib/python3.12/site-packages/google/genai/batches.py
new file mode 100644
index 00000000..6dd5b4c5
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/google/genai/batches.py
@@ -0,0 +1,1293 @@
+# 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 _extra_utils
+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 _BatchJobSource_to_mldev(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['format']) is not None:
+    raise ValueError('format parameter is not supported in Google AI.')
+
+  if getv(from_object, ['gcs_uri']) is not None:
+    raise ValueError('gcs_uri parameter is not supported in Google AI.')
+
+  if getv(from_object, ['bigquery_uri']) is not None:
+    raise ValueError('bigquery_uri parameter is not supported in Google AI.')
+
+  return to_object
+
+
+def _BatchJobSource_to_vertex(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['format']) is not None:
+    setv(to_object, ['instancesFormat'], getv(from_object, ['format']))
+
+  if getv(from_object, ['gcs_uri']) is not None:
+    setv(to_object, ['gcsSource', 'uris'], getv(from_object, ['gcs_uri']))
+
+  if getv(from_object, ['bigquery_uri']) is not None:
+    setv(
+        to_object,
+        ['bigquerySource', 'inputUri'],
+        getv(from_object, ['bigquery_uri']),
+    )
+
+  return to_object
+
+
+def _BatchJobDestination_to_mldev(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['format']) is not None:
+    raise ValueError('format parameter is not supported in Google AI.')
+
+  if getv(from_object, ['gcs_uri']) is not None:
+    raise ValueError('gcs_uri parameter is not supported in Google AI.')
+
+  if getv(from_object, ['bigquery_uri']) is not None:
+    raise ValueError('bigquery_uri parameter is not supported in Google AI.')
+
+  return to_object
+
+
+def _BatchJobDestination_to_vertex(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['format']) is not None:
+    setv(to_object, ['predictionsFormat'], getv(from_object, ['format']))
+
+  if getv(from_object, ['gcs_uri']) is not None:
+    setv(
+        to_object,
+        ['gcsDestination', 'outputUriPrefix'],
+        getv(from_object, ['gcs_uri']),
+    )
+
+  if getv(from_object, ['bigquery_uri']) is not None:
+    setv(
+        to_object,
+        ['bigqueryDestination', 'outputUri'],
+        getv(from_object, ['bigquery_uri']),
+    )
+
+  return to_object
+
+
+def _CreateBatchJobConfig_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, ['display_name']) is not None:
+    setv(parent_object, ['displayName'], getv(from_object, ['display_name']))
+
+  if getv(from_object, ['dest']) is not None:
+    raise ValueError('dest parameter is not supported in Google AI.')
+
+  return to_object
+
+
+def _CreateBatchJobConfig_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, ['display_name']) is not None:
+    setv(parent_object, ['displayName'], getv(from_object, ['display_name']))
+
+  if getv(from_object, ['dest']) is not None:
+    setv(
+        parent_object,
+        ['outputConfig'],
+        _BatchJobDestination_to_vertex(
+            api_client,
+            t.t_batch_job_destination(api_client, getv(from_object, ['dest'])),
+            to_object,
+        ),
+    )
+
+  return to_object
+
+
+def _CreateBatchJobParameters_to_mldev(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['model']) is not None:
+    raise ValueError('model parameter is not supported in Google AI.')
+
+  if getv(from_object, ['src']) is not None:
+    raise ValueError('src parameter is not supported in Google AI.')
+
+  if getv(from_object, ['config']) is not None:
+    setv(
+        to_object,
+        ['config'],
+        _CreateBatchJobConfig_to_mldev(
+            api_client, getv(from_object, ['config']), to_object
+        ),
+    )
+
+  return to_object
+
+
+def _CreateBatchJobParameters_to_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'],
+        t.t_model(api_client, getv(from_object, ['model'])),
+    )
+
+  if getv(from_object, ['src']) is not None:
+    setv(
+        to_object,
+        ['inputConfig'],
+        _BatchJobSource_to_vertex(
+            api_client,
+            t.t_batch_job_source(api_client, getv(from_object, ['src'])),
+            to_object,
+        ),
+    )
+
+  if getv(from_object, ['config']) is not None:
+    setv(
+        to_object,
+        ['config'],
+        _CreateBatchJobConfig_to_vertex(
+            api_client, getv(from_object, ['config']), to_object
+        ),
+    )
+
+  return to_object
+
+
+def _GetBatchJobConfig_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 _GetBatchJobConfig_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 _GetBatchJobParameters_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:
+    raise ValueError('name parameter is not supported in Google AI.')
+
+  if getv(from_object, ['config']) is not None:
+    setv(
+        to_object,
+        ['config'],
+        _GetBatchJobConfig_to_mldev(
+            api_client, getv(from_object, ['config']), to_object
+        ),
+    )
+
+  return to_object
+
+
+def _GetBatchJobParameters_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'],
+        t.t_batch_job_name(api_client, getv(from_object, ['name'])),
+    )
+
+  if getv(from_object, ['config']) is not None:
+    setv(
+        to_object,
+        ['config'],
+        _GetBatchJobConfig_to_vertex(
+            api_client, getv(from_object, ['config']), to_object
+        ),
+    )
+
+  return to_object
+
+
+def _CancelBatchJobConfig_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 _CancelBatchJobConfig_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 _CancelBatchJobParameters_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:
+    raise ValueError('name parameter is not supported in Google AI.')
+
+  if getv(from_object, ['config']) is not None:
+    setv(
+        to_object,
+        ['config'],
+        _CancelBatchJobConfig_to_mldev(
+            api_client, getv(from_object, ['config']), to_object
+        ),
+    )
+
+  return to_object
+
+
+def _CancelBatchJobParameters_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'],
+        t.t_batch_job_name(api_client, getv(from_object, ['name'])),
+    )
+
+  if getv(from_object, ['config']) is not None:
+    setv(
+        to_object,
+        ['config'],
+        _CancelBatchJobConfig_to_vertex(
+            api_client, getv(from_object, ['config']), to_object
+        ),
+    )
+
+  return to_object
+
+
+def _ListBatchJobConfig_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, ['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:
+    raise ValueError('filter parameter is not supported in Google AI.')
+
+  return to_object
+
+
+def _ListBatchJobConfig_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, ['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 _ListBatchJobParameters_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:
+    raise ValueError('config parameter is not supported in Google AI.')
+
+  return to_object
+
+
+def _ListBatchJobParameters_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'],
+        _ListBatchJobConfig_to_vertex(
+            api_client, getv(from_object, ['config']), to_object
+        ),
+    )
+
+  return to_object
+
+
+def _DeleteBatchJobParameters_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:
+    raise ValueError('name parameter is not supported in Google AI.')
+
+  return to_object
+
+
+def _DeleteBatchJobParameters_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'],
+        t.t_batch_job_name(api_client, getv(from_object, ['name'])),
+    )
+
+  return to_object
+
+
+def _JobError_from_mldev(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+
+  return to_object
+
+
+def _JobError_from_vertex(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['details']) is not None:
+    setv(to_object, ['details'], getv(from_object, ['details']))
+
+  if getv(from_object, ['code']) is not None:
+    setv(to_object, ['code'], getv(from_object, ['code']))
+
+  if getv(from_object, ['message']) is not None:
+    setv(to_object, ['message'], getv(from_object, ['message']))
+
+  return to_object
+
+
+def _BatchJobSource_from_mldev(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+
+  return to_object
+
+
+def _BatchJobSource_from_vertex(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['instancesFormat']) is not None:
+    setv(to_object, ['format'], getv(from_object, ['instancesFormat']))
+
+  if getv(from_object, ['gcsSource', 'uris']) is not None:
+    setv(to_object, ['gcs_uri'], getv(from_object, ['gcsSource', 'uris']))
+
+  if getv(from_object, ['bigquerySource', 'inputUri']) is not None:
+    setv(
+        to_object,
+        ['bigquery_uri'],
+        getv(from_object, ['bigquerySource', 'inputUri']),
+    )
+
+  return to_object
+
+
+def _BatchJobDestination_from_mldev(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+
+  return to_object
+
+
+def _BatchJobDestination_from_vertex(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+  if getv(from_object, ['predictionsFormat']) is not None:
+    setv(to_object, ['format'], getv(from_object, ['predictionsFormat']))
+
+  if getv(from_object, ['gcsDestination', 'outputUriPrefix']) is not None:
+    setv(
+        to_object,
+        ['gcs_uri'],
+        getv(from_object, ['gcsDestination', 'outputUriPrefix']),
+    )
+
+  if getv(from_object, ['bigqueryDestination', 'outputUri']) is not None:
+    setv(
+        to_object,
+        ['bigquery_uri'],
+        getv(from_object, ['bigqueryDestination', 'outputUri']),
+    )
+
+  return to_object
+
+
+def _BatchJob_from_mldev(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+
+  return to_object
+
+
+def _BatchJob_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, ['displayName']) is not None:
+    setv(to_object, ['display_name'], getv(from_object, ['displayName']))
+
+  if getv(from_object, ['state']) is not None:
+    setv(to_object, ['state'], getv(from_object, ['state']))
+
+  if getv(from_object, ['error']) is not None:
+    setv(
+        to_object,
+        ['error'],
+        _JobError_from_vertex(
+            api_client, getv(from_object, ['error']), to_object
+        ),
+    )
+
+  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, ['model']) is not None:
+    setv(to_object, ['model'], getv(from_object, ['model']))
+
+  if getv(from_object, ['inputConfig']) is not None:
+    setv(
+        to_object,
+        ['src'],
+        _BatchJobSource_from_vertex(
+            api_client, getv(from_object, ['inputConfig']), to_object
+        ),
+    )
+
+  if getv(from_object, ['outputConfig']) is not None:
+    setv(
+        to_object,
+        ['dest'],
+        _BatchJobDestination_from_vertex(
+            api_client, getv(from_object, ['outputConfig']), to_object
+        ),
+    )
+
+  return to_object
+
+
+def _ListBatchJobResponse_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']))
+
+  return to_object
+
+
+def _ListBatchJobResponse_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, ['batchPredictionJobs']) is not None:
+    setv(
+        to_object,
+        ['batch_jobs'],
+        [
+            _BatchJob_from_vertex(api_client, item, to_object)
+            for item in getv(from_object, ['batchPredictionJobs'])
+        ],
+    )
+
+  return to_object
+
+
+def _DeleteResourceJob_from_mldev(
+    api_client: ApiClient,
+    from_object: Union[dict, object],
+    parent_object: dict = None,
+) -> dict:
+  to_object = {}
+
+  return to_object
+
+
+def _DeleteResourceJob_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, ['done']) is not None:
+    setv(to_object, ['done'], getv(from_object, ['done']))
+
+  if getv(from_object, ['error']) is not None:
+    setv(
+        to_object,
+        ['error'],
+        _JobError_from_vertex(
+            api_client, getv(from_object, ['error']), to_object
+        ),
+    )
+
+  return to_object
+
+
+class Batches(_common.BaseModule):
+
+  def _create(
+      self,
+      *,
+      model: str,
+      src: str,
+      config: Optional[types.CreateBatchJobConfigOrDict] = None,
+  ) -> types.BatchJob:
+    parameter_model = types._CreateBatchJobParameters(
+        model=model,
+        src=src,
+        config=config,
+    )
+
+    if not self._api_client.vertexai:
+      raise ValueError('This method is only supported in the Vertex AI client.')
+    else:
+      request_dict = _CreateBatchJobParameters_to_vertex(
+          self._api_client, parameter_model
+      )
+      path = 'batchPredictionJobs'.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 = _BatchJob_from_vertex(self._api_client, response_dict)
+    else:
+      response_dict = _BatchJob_from_mldev(self._api_client, response_dict)
+
+    return_value = types.BatchJob._from_response(response_dict, parameter_model)
+    self._api_client._verify_response(return_value)
+    return return_value
+
+  def get(
+      self, *, name: str, config: Optional[types.GetBatchJobConfigOrDict] = None
+  ) -> types.BatchJob:
+    """Gets a batch job.
+
+    Args:
+      name (str): A fully-qualified BatchJob resource name or ID.
+        Example: "projects/.../locations/.../batchPredictionJobs/456" or "456"
+          when project and location are initialized in the client.
+
+    Returns:
+      A BatchJob object that contains details about the batch job.
+
+    Usage:
+
+    .. code-block:: python
+
+      batch_job = client.batches.get(name='123456789')
+      print(f"Batch job: {batch_job.name}, state {batch_job.state}")
+    """
+
+    parameter_model = types._GetBatchJobParameters(
+        name=name,
+        config=config,
+    )
+
+    if not self._api_client.vertexai:
+      raise ValueError('This method is only supported in the Vertex AI client.')
+    else:
+      request_dict = _GetBatchJobParameters_to_vertex(
+          self._api_client, parameter_model
+      )
+      path = 'batchPredictionJobs/{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 = _BatchJob_from_vertex(self._api_client, response_dict)
+    else:
+      response_dict = _BatchJob_from_mldev(self._api_client, response_dict)
+
+    return_value = types.BatchJob._from_response(response_dict, parameter_model)
+    self._api_client._verify_response(return_value)
+    return return_value
+
+  def cancel(
+      self,
+      *,
+      name: str,
+      config: Optional[types.CancelBatchJobConfigOrDict] = None,
+  ) -> None:
+    parameter_model = types._CancelBatchJobParameters(
+        name=name,
+        config=config,
+    )
+
+    if not self._api_client.vertexai:
+      raise ValueError('This method is only supported in the Vertex AI client.')
+    else:
+      request_dict = _CancelBatchJobParameters_to_vertex(
+          self._api_client, parameter_model
+      )
+      path = 'batchPredictionJobs/{name}:cancel'.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
+    )
+
+  def _list(
+      self, *, config: types.ListBatchJobConfigOrDict
+  ) -> types.ListBatchJobResponse:
+    parameter_model = types._ListBatchJobParameters(
+        config=config,
+    )
+
+    if not self._api_client.vertexai:
+      raise ValueError('This method is only supported in the Vertex AI client.')
+    else:
+      request_dict = _ListBatchJobParameters_to_vertex(
+          self._api_client, parameter_model
+      )
+      path = 'batchPredictionJobs'.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 = _ListBatchJobResponse_from_vertex(
+          self._api_client, response_dict
+      )
+    else:
+      response_dict = _ListBatchJobResponse_from_mldev(
+          self._api_client, response_dict
+      )
+
+    return_value = types.ListBatchJobResponse._from_response(
+        response_dict, parameter_model
+    )
+    self._api_client._verify_response(return_value)
+    return return_value
+
+  def delete(self, *, name: str) -> types.DeleteResourceJob:
+    """Deletes a batch job.
+
+    Args:
+      name (str): A fully-qualified BatchJob resource name or ID.
+        Example: "projects/.../locations/.../batchPredictionJobs/456" or "456"
+          when project and location are initialized in the client.
+
+    Returns:
+      A DeleteResourceJob object that shows the status of the deletion.
+
+    Usage:
+
+    .. code-block:: python
+
+      client.batches.delete(name='123456789')
+    """
+
+    parameter_model = types._DeleteBatchJobParameters(
+        name=name,
+    )
+
+    if not self._api_client.vertexai:
+      raise ValueError('This method is only supported in the Vertex AI client.')
+    else:
+      request_dict = _DeleteBatchJobParameters_to_vertex(
+          self._api_client, parameter_model
+      )
+      path = 'batchPredictionJobs/{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(
+        'delete', path, request_dict, http_options
+    )
+
+    if self._api_client.vertexai:
+      response_dict = _DeleteResourceJob_from_vertex(
+          self._api_client, response_dict
+      )
+    else:
+      response_dict = _DeleteResourceJob_from_mldev(
+          self._api_client, response_dict
+      )
+
+    return_value = types.DeleteResourceJob._from_response(
+        response_dict, parameter_model
+    )
+    self._api_client._verify_response(return_value)
+    return return_value
+
+  def create(
+      self,
+      *,
+      model: str,
+      src: str,
+      config: Optional[types.CreateBatchJobConfigOrDict] = None,
+  ) -> types.BatchJob:
+    """Creates a batch job.
+
+    Args:
+      model (str): The model to use for the batch job.
+      src (str): The source of the batch job. Currently supports GCS URI(-s) or
+        BigQuery URI. Example: "gs://path/to/input/data" or
+        "bq://projectId.bqDatasetId.bqTableId".
+      config (CreateBatchJobConfig): Optional configuration for the batch job.
+
+    Returns:
+      A BatchJob object that contains details about the batch job.
+
+    Usage:
+
+    .. code-block:: python
+
+      batch_job = client.batches.create(
+          model="gemini-1.5-flash",
+          src="gs://path/to/input/data",
+      )
+      print(batch_job.state)
+    """
+    config = _extra_utils.format_destination(src, config)
+    return self._create(model=model, src=src, config=config)
+
+  def list(
+      self, *, config: Optional[types.ListBatchJobConfigOrDict] = None
+  ) -> Pager[types.BatchJob]:
+    """Lists batch jobs.
+
+    Args:
+      config (ListBatchJobConfig): Optional configuration for the list request.
+
+    Returns:
+      A Pager object that contains one page of batch jobs. When iterating over
+      the pager, it automatically fetches the next page if there are more.
+
+    Usage:
+
+    .. code-block:: python
+
+      batch_jobs = client.batches.list(config={"page_size": 10})
+      for batch_job in batch_jobs:
+        print(f"Batch job: {batch_job.name}, state {batch_job.state}")
+    """
+    return Pager(
+        'batch_jobs',
+        self._list,
+        self._list(config=config),
+        config,
+    )
+
+
+class AsyncBatches(_common.BaseModule):
+
+  async def _create(
+      self,
+      *,
+      model: str,
+      src: str,
+      config: Optional[types.CreateBatchJobConfigOrDict] = None,
+  ) -> types.BatchJob:
+    parameter_model = types._CreateBatchJobParameters(
+        model=model,
+        src=src,
+        config=config,
+    )
+
+    if not self._api_client.vertexai:
+      raise ValueError('This method is only supported in the Vertex AI client.')
+    else:
+      request_dict = _CreateBatchJobParameters_to_vertex(
+          self._api_client, parameter_model
+      )
+      path = 'batchPredictionJobs'.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 = _BatchJob_from_vertex(self._api_client, response_dict)
+    else:
+      response_dict = _BatchJob_from_mldev(self._api_client, response_dict)
+
+    return_value = types.BatchJob._from_response(response_dict, parameter_model)
+    self._api_client._verify_response(return_value)
+    return return_value
+
+  async def get(
+      self, *, name: str, config: Optional[types.GetBatchJobConfigOrDict] = None
+  ) -> types.BatchJob:
+    """Gets a batch job.
+
+    Args:
+      name (str): A fully-qualified BatchJob resource name or ID.
+        Example: "projects/.../locations/.../batchPredictionJobs/456" or "456"
+          when project and location are initialized in the client.
+
+    Returns:
+      A BatchJob object that contains details about the batch job.
+
+    Usage:
+
+    .. code-block:: python
+
+      batch_job = client.batches.get(name='123456789')
+      print(f"Batch job: {batch_job.name}, state {batch_job.state}")
+    """
+
+    parameter_model = types._GetBatchJobParameters(
+        name=name,
+        config=config,
+    )
+
+    if not self._api_client.vertexai:
+      raise ValueError('This method is only supported in the Vertex AI client.')
+    else:
+      request_dict = _GetBatchJobParameters_to_vertex(
+          self._api_client, parameter_model
+      )
+      path = 'batchPredictionJobs/{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 = _BatchJob_from_vertex(self._api_client, response_dict)
+    else:
+      response_dict = _BatchJob_from_mldev(self._api_client, response_dict)
+
+    return_value = types.BatchJob._from_response(response_dict, parameter_model)
+    self._api_client._verify_response(return_value)
+    return return_value
+
+  async def cancel(
+      self,
+      *,
+      name: str,
+      config: Optional[types.CancelBatchJobConfigOrDict] = None,
+  ) -> None:
+    parameter_model = types._CancelBatchJobParameters(
+        name=name,
+        config=config,
+    )
+
+    if not self._api_client.vertexai:
+      raise ValueError('This method is only supported in the Vertex AI client.')
+    else:
+      request_dict = _CancelBatchJobParameters_to_vertex(
+          self._api_client, parameter_model
+      )
+      path = 'batchPredictionJobs/{name}:cancel'.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
+    )
+
+  async def _list(
+      self, *, config: types.ListBatchJobConfigOrDict
+  ) -> types.ListBatchJobResponse:
+    parameter_model = types._ListBatchJobParameters(
+        config=config,
+    )
+
+    if not self._api_client.vertexai:
+      raise ValueError('This method is only supported in the Vertex AI client.')
+    else:
+      request_dict = _ListBatchJobParameters_to_vertex(
+          self._api_client, parameter_model
+      )
+      path = 'batchPredictionJobs'.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 = _ListBatchJobResponse_from_vertex(
+          self._api_client, response_dict
+      )
+    else:
+      response_dict = _ListBatchJobResponse_from_mldev(
+          self._api_client, response_dict
+      )
+
+    return_value = types.ListBatchJobResponse._from_response(
+        response_dict, parameter_model
+    )
+    self._api_client._verify_response(return_value)
+    return return_value
+
+  async def delete(self, *, name: str) -> types.DeleteResourceJob:
+    """Deletes a batch job.
+
+    Args:
+      name (str): A fully-qualified BatchJob resource name or ID.
+        Example: "projects/.../locations/.../batchPredictionJobs/456" or "456"
+          when project and location are initialized in the client.
+
+    Returns:
+      A DeleteResourceJob object that shows the status of the deletion.
+
+    Usage:
+
+    .. code-block:: python
+
+      client.batches.delete(name='123456789')
+    """
+
+    parameter_model = types._DeleteBatchJobParameters(
+        name=name,
+    )
+
+    if not self._api_client.vertexai:
+      raise ValueError('This method is only supported in the Vertex AI client.')
+    else:
+      request_dict = _DeleteBatchJobParameters_to_vertex(
+          self._api_client, parameter_model
+      )
+      path = 'batchPredictionJobs/{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(
+        'delete', path, request_dict, http_options
+    )
+
+    if self._api_client.vertexai:
+      response_dict = _DeleteResourceJob_from_vertex(
+          self._api_client, response_dict
+      )
+    else:
+      response_dict = _DeleteResourceJob_from_mldev(
+          self._api_client, response_dict
+      )
+
+    return_value = types.DeleteResourceJob._from_response(
+        response_dict, parameter_model
+    )
+    self._api_client._verify_response(return_value)
+    return return_value
+
+  async def create(
+      self,
+      *,
+      model: str,
+      src: str,
+      config: Optional[types.CreateBatchJobConfigOrDict] = None,
+  ) -> types.BatchJob:
+    """Creates a batch job asynchronously.
+
+    Args:
+      model (str): The model to use for the batch job.
+      src (str): The source of the batch job. Currently supports GCS URI(-s) or
+        BigQuery URI. Example: "gs://path/to/input/data" or
+        "bq://projectId.bqDatasetId.bqTableId".
+      config (CreateBatchJobConfig): Optional configuration for the batch job.
+
+    Returns:
+      A BatchJob object that contains details about the batch job.
+
+    Usage:
+
+    .. code-block:: python
+
+      batch_job = await client.aio.batches.create(
+          model="gemini-1.5-flash",
+          src="gs://path/to/input/data",
+      )
+    """
+    config = _extra_utils.format_destination(src, config)
+    return await self._create(model=model, src=src, config=config)
+
+  async def list(
+      self, *, config: Optional[types.ListBatchJobConfigOrDict] = None
+  ) -> AsyncPager[types.BatchJob]:
+    """Lists batch jobs asynchronously.
+
+    Args:
+      config (ListBatchJobConfig): Optional configuration for the list request.
+
+    Returns:
+      A Pager object that contains one page of batch jobs. When iterating over
+      the pager, it automatically fetches the next page if there are more.
+
+    Usage:
+
+    .. code-block:: python
+
+      batch_jobs = await client.aio.batches.list(config={'page_size': 5})
+      print(f"current page: {batch_jobs.page}")
+      await batch_jobs_pager.next_page()
+      print(f"next page: {batch_jobs_pager.page}")
+    """
+    return AsyncPager(
+        'batch_jobs',
+        self._list,
+        await self._list(config=config),
+        config,
+    )