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authorS. Solomon Darnell2025-03-28 21:52:21 -0500
committerS. Solomon Darnell2025-03-28 21:52:21 -0500
commit4a52a71956a8d46fcb7294ac71734504bb09bcc2 (patch)
treeee3dc5af3b6313e921cd920906356f5d4febc4ed /.venv/lib/python3.12/site-packages/google/genai/models.py
parentcc961e04ba734dd72309fb548a2f97d67d578813 (diff)
downloadgn-ai-master.tar.gz
two version of R2R are hereHEADmaster
Diffstat (limited to '.venv/lib/python3.12/site-packages/google/genai/models.py')
-rw-r--r--.venv/lib/python3.12/site-packages/google/genai/models.py5567
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diff --git a/.venv/lib/python3.12/site-packages/google/genai/models.py b/.venv/lib/python3.12/site-packages/google/genai/models.py
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+++ b/.venv/lib/python3.12/site-packages/google/genai/models.py
@@ -0,0 +1,5567 @@
+# 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.
+
+import logging
+from typing import AsyncIterator, Iterator, 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, HttpOptionsDict
+from ._common import get_value_by_path as getv
+from ._common import set_value_by_path as setv
+from .pagers import AsyncPager, Pager
+
+
+def _Part_to_mldev(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['video_metadata']) is not None:
+ raise ValueError('video_metadata parameter is not supported in Google AI.')
+
+ if getv(from_object, ['thought']) is not None:
+ setv(to_object, ['thought'], getv(from_object, ['thought']))
+
+ if getv(from_object, ['code_execution_result']) is not None:
+ setv(
+ to_object,
+ ['codeExecutionResult'],
+ getv(from_object, ['code_execution_result']),
+ )
+
+ if getv(from_object, ['executable_code']) is not None:
+ setv(to_object, ['executableCode'], getv(from_object, ['executable_code']))
+
+ if getv(from_object, ['file_data']) is not None:
+ setv(to_object, ['fileData'], getv(from_object, ['file_data']))
+
+ if getv(from_object, ['function_call']) is not None:
+ setv(to_object, ['functionCall'], getv(from_object, ['function_call']))
+
+ if getv(from_object, ['function_response']) is not None:
+ setv(
+ to_object,
+ ['functionResponse'],
+ getv(from_object, ['function_response']),
+ )
+
+ if getv(from_object, ['inline_data']) is not None:
+ setv(to_object, ['inlineData'], getv(from_object, ['inline_data']))
+
+ if getv(from_object, ['text']) is not None:
+ setv(to_object, ['text'], getv(from_object, ['text']))
+
+ return to_object
+
+
+def _Part_to_vertex(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['video_metadata']) is not None:
+ setv(to_object, ['videoMetadata'], getv(from_object, ['video_metadata']))
+
+ if getv(from_object, ['thought']) is not None:
+ setv(to_object, ['thought'], getv(from_object, ['thought']))
+
+ if getv(from_object, ['code_execution_result']) is not None:
+ setv(
+ to_object,
+ ['codeExecutionResult'],
+ getv(from_object, ['code_execution_result']),
+ )
+
+ if getv(from_object, ['executable_code']) is not None:
+ setv(to_object, ['executableCode'], getv(from_object, ['executable_code']))
+
+ if getv(from_object, ['file_data']) is not None:
+ setv(to_object, ['fileData'], getv(from_object, ['file_data']))
+
+ if getv(from_object, ['function_call']) is not None:
+ setv(to_object, ['functionCall'], getv(from_object, ['function_call']))
+
+ if getv(from_object, ['function_response']) is not None:
+ setv(
+ to_object,
+ ['functionResponse'],
+ getv(from_object, ['function_response']),
+ )
+
+ if getv(from_object, ['inline_data']) is not None:
+ setv(to_object, ['inlineData'], getv(from_object, ['inline_data']))
+
+ if getv(from_object, ['text']) is not None:
+ setv(to_object, ['text'], getv(from_object, ['text']))
+
+ return to_object
+
+
+def _Content_to_mldev(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['parts']) is not None:
+ setv(
+ to_object,
+ ['parts'],
+ [
+ _Part_to_mldev(api_client, item, to_object)
+ for item in getv(from_object, ['parts'])
+ ],
+ )
+
+ if getv(from_object, ['role']) is not None:
+ setv(to_object, ['role'], getv(from_object, ['role']))
+
+ return to_object
+
+
+def _Content_to_vertex(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['parts']) is not None:
+ setv(
+ to_object,
+ ['parts'],
+ [
+ _Part_to_vertex(api_client, item, to_object)
+ for item in getv(from_object, ['parts'])
+ ],
+ )
+
+ if getv(from_object, ['role']) is not None:
+ setv(to_object, ['role'], getv(from_object, ['role']))
+
+ return to_object
+
+
+def _Schema_to_mldev(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['min_items']) is not None:
+ raise ValueError('min_items parameter is not supported in Google AI.')
+
+ if getv(from_object, ['example']) is not None:
+ raise ValueError('example parameter is not supported in Google AI.')
+
+ if getv(from_object, ['property_ordering']) is not None:
+ raise ValueError(
+ 'property_ordering parameter is not supported in Google AI.'
+ )
+
+ if getv(from_object, ['pattern']) is not None:
+ raise ValueError('pattern parameter is not supported in Google AI.')
+
+ if getv(from_object, ['minimum']) is not None:
+ raise ValueError('minimum parameter is not supported in Google AI.')
+
+ if getv(from_object, ['default']) is not None:
+ raise ValueError('default parameter is not supported in Google AI.')
+
+ if getv(from_object, ['any_of']) is not None:
+ raise ValueError('any_of parameter is not supported in Google AI.')
+
+ if getv(from_object, ['max_length']) is not None:
+ raise ValueError('max_length parameter is not supported in Google AI.')
+
+ if getv(from_object, ['title']) is not None:
+ raise ValueError('title parameter is not supported in Google AI.')
+
+ if getv(from_object, ['min_length']) is not None:
+ raise ValueError('min_length parameter is not supported in Google AI.')
+
+ if getv(from_object, ['min_properties']) is not None:
+ raise ValueError('min_properties parameter is not supported in Google AI.')
+
+ if getv(from_object, ['max_items']) is not None:
+ raise ValueError('max_items parameter is not supported in Google AI.')
+
+ if getv(from_object, ['maximum']) is not None:
+ raise ValueError('maximum parameter is not supported in Google AI.')
+
+ if getv(from_object, ['nullable']) is not None:
+ raise ValueError('nullable parameter is not supported in Google AI.')
+
+ if getv(from_object, ['max_properties']) is not None:
+ raise ValueError('max_properties parameter is not supported in Google AI.')
+
+ if getv(from_object, ['type']) is not None:
+ setv(to_object, ['type'], getv(from_object, ['type']))
+
+ if getv(from_object, ['description']) is not None:
+ setv(to_object, ['description'], getv(from_object, ['description']))
+
+ if getv(from_object, ['enum']) is not None:
+ setv(to_object, ['enum'], getv(from_object, ['enum']))
+
+ if getv(from_object, ['format']) is not None:
+ setv(to_object, ['format'], getv(from_object, ['format']))
+
+ if getv(from_object, ['items']) is not None:
+ setv(to_object, ['items'], getv(from_object, ['items']))
+
+ if getv(from_object, ['properties']) is not None:
+ setv(to_object, ['properties'], getv(from_object, ['properties']))
+
+ if getv(from_object, ['required']) is not None:
+ setv(to_object, ['required'], getv(from_object, ['required']))
+
+ return to_object
+
+
+def _Schema_to_vertex(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['min_items']) is not None:
+ setv(to_object, ['minItems'], getv(from_object, ['min_items']))
+
+ if getv(from_object, ['example']) is not None:
+ setv(to_object, ['example'], getv(from_object, ['example']))
+
+ if getv(from_object, ['property_ordering']) is not None:
+ setv(
+ to_object,
+ ['propertyOrdering'],
+ getv(from_object, ['property_ordering']),
+ )
+
+ if getv(from_object, ['pattern']) is not None:
+ setv(to_object, ['pattern'], getv(from_object, ['pattern']))
+
+ if getv(from_object, ['minimum']) is not None:
+ setv(to_object, ['minimum'], getv(from_object, ['minimum']))
+
+ if getv(from_object, ['default']) is not None:
+ setv(to_object, ['default'], getv(from_object, ['default']))
+
+ if getv(from_object, ['any_of']) is not None:
+ setv(to_object, ['anyOf'], getv(from_object, ['any_of']))
+
+ if getv(from_object, ['max_length']) is not None:
+ setv(to_object, ['maxLength'], getv(from_object, ['max_length']))
+
+ if getv(from_object, ['title']) is not None:
+ setv(to_object, ['title'], getv(from_object, ['title']))
+
+ if getv(from_object, ['min_length']) is not None:
+ setv(to_object, ['minLength'], getv(from_object, ['min_length']))
+
+ if getv(from_object, ['min_properties']) is not None:
+ setv(to_object, ['minProperties'], getv(from_object, ['min_properties']))
+
+ if getv(from_object, ['max_items']) is not None:
+ setv(to_object, ['maxItems'], getv(from_object, ['max_items']))
+
+ if getv(from_object, ['maximum']) is not None:
+ setv(to_object, ['maximum'], getv(from_object, ['maximum']))
+
+ if getv(from_object, ['nullable']) is not None:
+ setv(to_object, ['nullable'], getv(from_object, ['nullable']))
+
+ if getv(from_object, ['max_properties']) is not None:
+ setv(to_object, ['maxProperties'], getv(from_object, ['max_properties']))
+
+ if getv(from_object, ['type']) is not None:
+ setv(to_object, ['type'], getv(from_object, ['type']))
+
+ if getv(from_object, ['description']) is not None:
+ setv(to_object, ['description'], getv(from_object, ['description']))
+
+ if getv(from_object, ['enum']) is not None:
+ setv(to_object, ['enum'], getv(from_object, ['enum']))
+
+ if getv(from_object, ['format']) is not None:
+ setv(to_object, ['format'], getv(from_object, ['format']))
+
+ if getv(from_object, ['items']) is not None:
+ setv(to_object, ['items'], getv(from_object, ['items']))
+
+ if getv(from_object, ['properties']) is not None:
+ setv(to_object, ['properties'], getv(from_object, ['properties']))
+
+ if getv(from_object, ['required']) is not None:
+ setv(to_object, ['required'], getv(from_object, ['required']))
+
+ return to_object
+
+
+def _SafetySetting_to_mldev(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['method']) is not None:
+ raise ValueError('method parameter is not supported in Google AI.')
+
+ if getv(from_object, ['category']) is not None:
+ setv(to_object, ['category'], getv(from_object, ['category']))
+
+ if getv(from_object, ['threshold']) is not None:
+ setv(to_object, ['threshold'], getv(from_object, ['threshold']))
+
+ return to_object
+
+
+def _SafetySetting_to_vertex(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['method']) is not None:
+ setv(to_object, ['method'], getv(from_object, ['method']))
+
+ if getv(from_object, ['category']) is not None:
+ setv(to_object, ['category'], getv(from_object, ['category']))
+
+ if getv(from_object, ['threshold']) is not None:
+ setv(to_object, ['threshold'], getv(from_object, ['threshold']))
+
+ return to_object
+
+
+def _FunctionDeclaration_to_mldev(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['response']) is not None:
+ raise ValueError('response parameter is not supported in Google AI.')
+
+ if getv(from_object, ['description']) is not None:
+ setv(to_object, ['description'], getv(from_object, ['description']))
+
+ if getv(from_object, ['name']) is not None:
+ setv(to_object, ['name'], getv(from_object, ['name']))
+
+ if getv(from_object, ['parameters']) is not None:
+ setv(to_object, ['parameters'], getv(from_object, ['parameters']))
+
+ return to_object
+
+
+def _FunctionDeclaration_to_vertex(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['response']) is not None:
+ setv(
+ to_object,
+ ['response'],
+ _Schema_to_vertex(
+ api_client, getv(from_object, ['response']), to_object
+ ),
+ )
+
+ if getv(from_object, ['description']) is not None:
+ setv(to_object, ['description'], getv(from_object, ['description']))
+
+ if getv(from_object, ['name']) is not None:
+ setv(to_object, ['name'], getv(from_object, ['name']))
+
+ if getv(from_object, ['parameters']) is not None:
+ setv(to_object, ['parameters'], getv(from_object, ['parameters']))
+
+ return to_object
+
+
+def _GoogleSearch_to_mldev(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+
+ return to_object
+
+
+def _GoogleSearch_to_vertex(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+
+ return to_object
+
+
+def _DynamicRetrievalConfig_to_mldev(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['mode']) is not None:
+ setv(to_object, ['mode'], getv(from_object, ['mode']))
+
+ if getv(from_object, ['dynamic_threshold']) is not None:
+ setv(
+ to_object,
+ ['dynamicThreshold'],
+ getv(from_object, ['dynamic_threshold']),
+ )
+
+ return to_object
+
+
+def _DynamicRetrievalConfig_to_vertex(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['mode']) is not None:
+ setv(to_object, ['mode'], getv(from_object, ['mode']))
+
+ if getv(from_object, ['dynamic_threshold']) is not None:
+ setv(
+ to_object,
+ ['dynamicThreshold'],
+ getv(from_object, ['dynamic_threshold']),
+ )
+
+ return to_object
+
+
+def _GoogleSearchRetrieval_to_mldev(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['dynamic_retrieval_config']) is not None:
+ setv(
+ to_object,
+ ['dynamicRetrievalConfig'],
+ _DynamicRetrievalConfig_to_mldev(
+ api_client,
+ getv(from_object, ['dynamic_retrieval_config']),
+ to_object,
+ ),
+ )
+
+ return to_object
+
+
+def _GoogleSearchRetrieval_to_vertex(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['dynamic_retrieval_config']) is not None:
+ setv(
+ to_object,
+ ['dynamicRetrievalConfig'],
+ _DynamicRetrievalConfig_to_vertex(
+ api_client,
+ getv(from_object, ['dynamic_retrieval_config']),
+ to_object,
+ ),
+ )
+
+ return to_object
+
+
+def _Tool_to_mldev(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['function_declarations']) is not None:
+ setv(
+ to_object,
+ ['functionDeclarations'],
+ [
+ _FunctionDeclaration_to_mldev(api_client, item, to_object)
+ for item in getv(from_object, ['function_declarations'])
+ ],
+ )
+
+ if getv(from_object, ['retrieval']) is not None:
+ raise ValueError('retrieval parameter is not supported in Google AI.')
+
+ if getv(from_object, ['google_search']) is not None:
+ setv(
+ to_object,
+ ['googleSearch'],
+ _GoogleSearch_to_mldev(
+ api_client, getv(from_object, ['google_search']), to_object
+ ),
+ )
+
+ if getv(from_object, ['google_search_retrieval']) is not None:
+ setv(
+ to_object,
+ ['googleSearchRetrieval'],
+ _GoogleSearchRetrieval_to_mldev(
+ api_client,
+ getv(from_object, ['google_search_retrieval']),
+ to_object,
+ ),
+ )
+
+ if getv(from_object, ['code_execution']) is not None:
+ setv(to_object, ['codeExecution'], getv(from_object, ['code_execution']))
+
+ return to_object
+
+
+def _Tool_to_vertex(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['function_declarations']) is not None:
+ setv(
+ to_object,
+ ['functionDeclarations'],
+ [
+ _FunctionDeclaration_to_vertex(api_client, item, to_object)
+ for item in getv(from_object, ['function_declarations'])
+ ],
+ )
+
+ if getv(from_object, ['retrieval']) is not None:
+ setv(to_object, ['retrieval'], getv(from_object, ['retrieval']))
+
+ if getv(from_object, ['google_search']) is not None:
+ setv(
+ to_object,
+ ['googleSearch'],
+ _GoogleSearch_to_vertex(
+ api_client, getv(from_object, ['google_search']), to_object
+ ),
+ )
+
+ if getv(from_object, ['google_search_retrieval']) is not None:
+ setv(
+ to_object,
+ ['googleSearchRetrieval'],
+ _GoogleSearchRetrieval_to_vertex(
+ api_client,
+ getv(from_object, ['google_search_retrieval']),
+ to_object,
+ ),
+ )
+
+ if getv(from_object, ['code_execution']) is not None:
+ setv(to_object, ['codeExecution'], getv(from_object, ['code_execution']))
+
+ return to_object
+
+
+def _FunctionCallingConfig_to_mldev(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['mode']) is not None:
+ setv(to_object, ['mode'], getv(from_object, ['mode']))
+
+ if getv(from_object, ['allowed_function_names']) is not None:
+ setv(
+ to_object,
+ ['allowedFunctionNames'],
+ getv(from_object, ['allowed_function_names']),
+ )
+
+ return to_object
+
+
+def _FunctionCallingConfig_to_vertex(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['mode']) is not None:
+ setv(to_object, ['mode'], getv(from_object, ['mode']))
+
+ if getv(from_object, ['allowed_function_names']) is not None:
+ setv(
+ to_object,
+ ['allowedFunctionNames'],
+ getv(from_object, ['allowed_function_names']),
+ )
+
+ return to_object
+
+
+def _ToolConfig_to_mldev(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['function_calling_config']) is not None:
+ setv(
+ to_object,
+ ['functionCallingConfig'],
+ _FunctionCallingConfig_to_mldev(
+ api_client,
+ getv(from_object, ['function_calling_config']),
+ to_object,
+ ),
+ )
+
+ return to_object
+
+
+def _ToolConfig_to_vertex(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['function_calling_config']) is not None:
+ setv(
+ to_object,
+ ['functionCallingConfig'],
+ _FunctionCallingConfig_to_vertex(
+ api_client,
+ getv(from_object, ['function_calling_config']),
+ to_object,
+ ),
+ )
+
+ return to_object
+
+
+def _PrebuiltVoiceConfig_to_mldev(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['voice_name']) is not None:
+ setv(to_object, ['voiceName'], getv(from_object, ['voice_name']))
+
+ return to_object
+
+
+def _PrebuiltVoiceConfig_to_vertex(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['voice_name']) is not None:
+ setv(to_object, ['voiceName'], getv(from_object, ['voice_name']))
+
+ return to_object
+
+
+def _VoiceConfig_to_mldev(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['prebuilt_voice_config']) is not None:
+ setv(
+ to_object,
+ ['prebuiltVoiceConfig'],
+ _PrebuiltVoiceConfig_to_mldev(
+ api_client, getv(from_object, ['prebuilt_voice_config']), to_object
+ ),
+ )
+
+ return to_object
+
+
+def _VoiceConfig_to_vertex(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['prebuilt_voice_config']) is not None:
+ setv(
+ to_object,
+ ['prebuiltVoiceConfig'],
+ _PrebuiltVoiceConfig_to_vertex(
+ api_client, getv(from_object, ['prebuilt_voice_config']), to_object
+ ),
+ )
+
+ return to_object
+
+
+def _SpeechConfig_to_mldev(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['voice_config']) is not None:
+ setv(
+ to_object,
+ ['voiceConfig'],
+ _VoiceConfig_to_mldev(
+ api_client, getv(from_object, ['voice_config']), to_object
+ ),
+ )
+
+ return to_object
+
+
+def _SpeechConfig_to_vertex(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['voice_config']) is not None:
+ setv(
+ to_object,
+ ['voiceConfig'],
+ _VoiceConfig_to_vertex(
+ api_client, getv(from_object, ['voice_config']), to_object
+ ),
+ )
+
+ return to_object
+
+
+def _ThinkingConfig_to_mldev(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['include_thoughts']) is not None:
+ setv(
+ to_object, ['includeThoughts'], getv(from_object, ['include_thoughts'])
+ )
+
+ return to_object
+
+
+def _ThinkingConfig_to_vertex(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['include_thoughts']) is not None:
+ setv(
+ to_object, ['includeThoughts'], getv(from_object, ['include_thoughts'])
+ )
+
+ return to_object
+
+
+def _GenerateContentConfig_to_mldev(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['system_instruction']) is not None:
+ setv(
+ parent_object,
+ ['systemInstruction'],
+ _Content_to_mldev(
+ api_client,
+ t.t_content(api_client, getv(from_object, ['system_instruction'])),
+ to_object,
+ ),
+ )
+
+ if getv(from_object, ['temperature']) is not None:
+ setv(to_object, ['temperature'], getv(from_object, ['temperature']))
+
+ if getv(from_object, ['top_p']) is not None:
+ setv(to_object, ['topP'], getv(from_object, ['top_p']))
+
+ if getv(from_object, ['top_k']) is not None:
+ setv(to_object, ['topK'], getv(from_object, ['top_k']))
+
+ if getv(from_object, ['candidate_count']) is not None:
+ setv(to_object, ['candidateCount'], getv(from_object, ['candidate_count']))
+
+ if getv(from_object, ['max_output_tokens']) is not None:
+ setv(
+ to_object, ['maxOutputTokens'], getv(from_object, ['max_output_tokens'])
+ )
+
+ if getv(from_object, ['stop_sequences']) is not None:
+ setv(to_object, ['stopSequences'], getv(from_object, ['stop_sequences']))
+
+ if getv(from_object, ['response_logprobs']) is not None:
+ setv(
+ to_object,
+ ['responseLogprobs'],
+ getv(from_object, ['response_logprobs']),
+ )
+
+ if getv(from_object, ['logprobs']) is not None:
+ setv(to_object, ['logprobs'], getv(from_object, ['logprobs']))
+
+ if getv(from_object, ['presence_penalty']) is not None:
+ setv(
+ to_object, ['presencePenalty'], getv(from_object, ['presence_penalty'])
+ )
+
+ if getv(from_object, ['frequency_penalty']) is not None:
+ setv(
+ to_object,
+ ['frequencyPenalty'],
+ getv(from_object, ['frequency_penalty']),
+ )
+
+ if getv(from_object, ['seed']) is not None:
+ setv(to_object, ['seed'], getv(from_object, ['seed']))
+
+ if getv(from_object, ['response_mime_type']) is not None:
+ setv(
+ to_object,
+ ['responseMimeType'],
+ getv(from_object, ['response_mime_type']),
+ )
+
+ if getv(from_object, ['response_schema']) is not None:
+ setv(
+ to_object,
+ ['responseSchema'],
+ _Schema_to_mldev(
+ api_client,
+ t.t_schema(api_client, getv(from_object, ['response_schema'])),
+ to_object,
+ ),
+ )
+
+ if getv(from_object, ['routing_config']) is not None:
+ raise ValueError('routing_config parameter is not supported in Google AI.')
+
+ if getv(from_object, ['safety_settings']) is not None:
+ setv(
+ parent_object,
+ ['safetySettings'],
+ [
+ _SafetySetting_to_mldev(api_client, item, to_object)
+ for item in getv(from_object, ['safety_settings'])
+ ],
+ )
+
+ if getv(from_object, ['tools']) is not None:
+ setv(
+ parent_object,
+ ['tools'],
+ [
+ _Tool_to_mldev(api_client, t.t_tool(api_client, item), to_object)
+ for item in t.t_tools(api_client, getv(from_object, ['tools']))
+ ],
+ )
+
+ if getv(from_object, ['tool_config']) is not None:
+ setv(
+ parent_object,
+ ['toolConfig'],
+ _ToolConfig_to_mldev(
+ api_client, getv(from_object, ['tool_config']), to_object
+ ),
+ )
+
+ if getv(from_object, ['cached_content']) is not None:
+ setv(
+ parent_object,
+ ['cachedContent'],
+ t.t_cached_content_name(
+ api_client, getv(from_object, ['cached_content'])
+ ),
+ )
+
+ if getv(from_object, ['response_modalities']) is not None:
+ setv(
+ to_object,
+ ['responseModalities'],
+ getv(from_object, ['response_modalities']),
+ )
+
+ if getv(from_object, ['media_resolution']) is not None:
+ raise ValueError(
+ 'media_resolution parameter is not supported in Google AI.'
+ )
+
+ if getv(from_object, ['speech_config']) is not None:
+ setv(
+ to_object,
+ ['speechConfig'],
+ _SpeechConfig_to_mldev(
+ api_client,
+ t.t_speech_config(api_client, getv(from_object, ['speech_config'])),
+ to_object,
+ ),
+ )
+
+ if getv(from_object, ['audio_timestamp']) is not None:
+ raise ValueError('audio_timestamp parameter is not supported in Google AI.')
+
+ if getv(from_object, ['thinking_config']) is not None:
+ setv(
+ to_object,
+ ['thinkingConfig'],
+ _ThinkingConfig_to_mldev(
+ api_client, getv(from_object, ['thinking_config']), to_object
+ ),
+ )
+
+ return to_object
+
+
+def _GenerateContentConfig_to_vertex(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['system_instruction']) is not None:
+ setv(
+ parent_object,
+ ['systemInstruction'],
+ _Content_to_vertex(
+ api_client,
+ t.t_content(api_client, getv(from_object, ['system_instruction'])),
+ to_object,
+ ),
+ )
+
+ if getv(from_object, ['temperature']) is not None:
+ setv(to_object, ['temperature'], getv(from_object, ['temperature']))
+
+ if getv(from_object, ['top_p']) is not None:
+ setv(to_object, ['topP'], getv(from_object, ['top_p']))
+
+ if getv(from_object, ['top_k']) is not None:
+ setv(to_object, ['topK'], getv(from_object, ['top_k']))
+
+ if getv(from_object, ['candidate_count']) is not None:
+ setv(to_object, ['candidateCount'], getv(from_object, ['candidate_count']))
+
+ if getv(from_object, ['max_output_tokens']) is not None:
+ setv(
+ to_object, ['maxOutputTokens'], getv(from_object, ['max_output_tokens'])
+ )
+
+ if getv(from_object, ['stop_sequences']) is not None:
+ setv(to_object, ['stopSequences'], getv(from_object, ['stop_sequences']))
+
+ if getv(from_object, ['response_logprobs']) is not None:
+ setv(
+ to_object,
+ ['responseLogprobs'],
+ getv(from_object, ['response_logprobs']),
+ )
+
+ if getv(from_object, ['logprobs']) is not None:
+ setv(to_object, ['logprobs'], getv(from_object, ['logprobs']))
+
+ if getv(from_object, ['presence_penalty']) is not None:
+ setv(
+ to_object, ['presencePenalty'], getv(from_object, ['presence_penalty'])
+ )
+
+ if getv(from_object, ['frequency_penalty']) is not None:
+ setv(
+ to_object,
+ ['frequencyPenalty'],
+ getv(from_object, ['frequency_penalty']),
+ )
+
+ if getv(from_object, ['seed']) is not None:
+ setv(to_object, ['seed'], getv(from_object, ['seed']))
+
+ if getv(from_object, ['response_mime_type']) is not None:
+ setv(
+ to_object,
+ ['responseMimeType'],
+ getv(from_object, ['response_mime_type']),
+ )
+
+ if getv(from_object, ['response_schema']) is not None:
+ setv(
+ to_object,
+ ['responseSchema'],
+ _Schema_to_vertex(
+ api_client,
+ t.t_schema(api_client, getv(from_object, ['response_schema'])),
+ to_object,
+ ),
+ )
+
+ if getv(from_object, ['routing_config']) is not None:
+ setv(to_object, ['routingConfig'], getv(from_object, ['routing_config']))
+
+ if getv(from_object, ['safety_settings']) is not None:
+ setv(
+ parent_object,
+ ['safetySettings'],
+ [
+ _SafetySetting_to_vertex(api_client, item, to_object)
+ for item in getv(from_object, ['safety_settings'])
+ ],
+ )
+
+ if getv(from_object, ['tools']) is not None:
+ setv(
+ parent_object,
+ ['tools'],
+ [
+ _Tool_to_vertex(api_client, t.t_tool(api_client, item), to_object)
+ for item in t.t_tools(api_client, getv(from_object, ['tools']))
+ ],
+ )
+
+ if getv(from_object, ['tool_config']) is not None:
+ setv(
+ parent_object,
+ ['toolConfig'],
+ _ToolConfig_to_vertex(
+ api_client, getv(from_object, ['tool_config']), to_object
+ ),
+ )
+
+ if getv(from_object, ['cached_content']) is not None:
+ setv(
+ parent_object,
+ ['cachedContent'],
+ t.t_cached_content_name(
+ api_client, getv(from_object, ['cached_content'])
+ ),
+ )
+
+ if getv(from_object, ['response_modalities']) is not None:
+ setv(
+ to_object,
+ ['responseModalities'],
+ getv(from_object, ['response_modalities']),
+ )
+
+ if getv(from_object, ['media_resolution']) is not None:
+ setv(
+ to_object, ['mediaResolution'], getv(from_object, ['media_resolution'])
+ )
+
+ if getv(from_object, ['speech_config']) is not None:
+ setv(
+ to_object,
+ ['speechConfig'],
+ _SpeechConfig_to_vertex(
+ api_client,
+ t.t_speech_config(api_client, getv(from_object, ['speech_config'])),
+ to_object,
+ ),
+ )
+
+ if getv(from_object, ['audio_timestamp']) is not None:
+ setv(to_object, ['audioTimestamp'], getv(from_object, ['audio_timestamp']))
+
+ if getv(from_object, ['thinking_config']) is not None:
+ setv(
+ to_object,
+ ['thinkingConfig'],
+ _ThinkingConfig_to_vertex(
+ api_client, getv(from_object, ['thinking_config']), to_object
+ ),
+ )
+
+ return to_object
+
+
+def _GenerateContentParameters_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:
+ setv(
+ to_object,
+ ['_url', 'model'],
+ t.t_model(api_client, getv(from_object, ['model'])),
+ )
+
+ if getv(from_object, ['contents']) is not None:
+ setv(
+ to_object,
+ ['contents'],
+ [
+ _Content_to_mldev(api_client, item, to_object)
+ for item in t.t_contents(
+ api_client, getv(from_object, ['contents'])
+ )
+ ],
+ )
+
+ if getv(from_object, ['config']) is not None:
+ setv(
+ to_object,
+ ['generationConfig'],
+ _GenerateContentConfig_to_mldev(
+ api_client, getv(from_object, ['config']), to_object
+ ),
+ )
+
+ return to_object
+
+
+def _GenerateContentParameters_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,
+ ['_url', 'model'],
+ t.t_model(api_client, getv(from_object, ['model'])),
+ )
+
+ if getv(from_object, ['contents']) is not None:
+ setv(
+ to_object,
+ ['contents'],
+ [
+ _Content_to_vertex(api_client, item, to_object)
+ for item in t.t_contents(
+ api_client, getv(from_object, ['contents'])
+ )
+ ],
+ )
+
+ if getv(from_object, ['config']) is not None:
+ setv(
+ to_object,
+ ['generationConfig'],
+ _GenerateContentConfig_to_vertex(
+ api_client, getv(from_object, ['config']), to_object
+ ),
+ )
+
+ return to_object
+
+
+def _EmbedContentConfig_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, ['task_type']) is not None:
+ setv(
+ parent_object,
+ ['requests[]', 'taskType'],
+ getv(from_object, ['task_type']),
+ )
+
+ if getv(from_object, ['title']) is not None:
+ setv(parent_object, ['requests[]', 'title'], getv(from_object, ['title']))
+
+ if getv(from_object, ['output_dimensionality']) is not None:
+ setv(
+ parent_object,
+ ['requests[]', 'outputDimensionality'],
+ getv(from_object, ['output_dimensionality']),
+ )
+
+ if getv(from_object, ['mime_type']) is not None:
+ raise ValueError('mime_type parameter is not supported in Google AI.')
+
+ if getv(from_object, ['auto_truncate']) is not None:
+ raise ValueError('auto_truncate parameter is not supported in Google AI.')
+
+ return to_object
+
+
+def _EmbedContentConfig_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, ['task_type']) is not None:
+ setv(
+ parent_object,
+ ['instances[]', 'task_type'],
+ getv(from_object, ['task_type']),
+ )
+
+ if getv(from_object, ['title']) is not None:
+ setv(parent_object, ['instances[]', 'title'], getv(from_object, ['title']))
+
+ if getv(from_object, ['output_dimensionality']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'outputDimensionality'],
+ getv(from_object, ['output_dimensionality']),
+ )
+
+ if getv(from_object, ['mime_type']) is not None:
+ setv(
+ parent_object,
+ ['instances[]', 'mimeType'],
+ getv(from_object, ['mime_type']),
+ )
+
+ if getv(from_object, ['auto_truncate']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'autoTruncate'],
+ getv(from_object, ['auto_truncate']),
+ )
+
+ return to_object
+
+
+def _EmbedContentParameters_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:
+ setv(
+ to_object,
+ ['_url', 'model'],
+ t.t_model(api_client, getv(from_object, ['model'])),
+ )
+
+ if getv(from_object, ['contents']) is not None:
+ setv(
+ to_object,
+ ['requests[]', 'content'],
+ t.t_contents_for_embed(api_client, getv(from_object, ['contents'])),
+ )
+
+ if getv(from_object, ['config']) is not None:
+ setv(
+ to_object,
+ ['config'],
+ _EmbedContentConfig_to_mldev(
+ api_client, getv(from_object, ['config']), to_object
+ ),
+ )
+
+ setv(
+ to_object,
+ ['requests[]', 'model'],
+ t.t_model(api_client, getv(from_object, ['model'])),
+ )
+ return to_object
+
+
+def _EmbedContentParameters_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,
+ ['_url', 'model'],
+ t.t_model(api_client, getv(from_object, ['model'])),
+ )
+
+ if getv(from_object, ['contents']) is not None:
+ setv(
+ to_object,
+ ['instances[]', 'content'],
+ t.t_contents_for_embed(api_client, getv(from_object, ['contents'])),
+ )
+
+ if getv(from_object, ['config']) is not None:
+ setv(
+ to_object,
+ ['config'],
+ _EmbedContentConfig_to_vertex(
+ api_client, getv(from_object, ['config']), to_object
+ ),
+ )
+
+ return to_object
+
+
+def _GenerateImageConfig_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, ['output_gcs_uri']) is not None:
+ raise ValueError('output_gcs_uri parameter is not supported in Google AI.')
+
+ if getv(from_object, ['negative_prompt']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'negativePrompt'],
+ getv(from_object, ['negative_prompt']),
+ )
+
+ if getv(from_object, ['number_of_images']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'sampleCount'],
+ getv(from_object, ['number_of_images']),
+ )
+
+ if getv(from_object, ['guidance_scale']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'guidanceScale'],
+ getv(from_object, ['guidance_scale']),
+ )
+
+ if getv(from_object, ['seed']) is not None:
+ raise ValueError('seed parameter is not supported in Google AI.')
+
+ if getv(from_object, ['safety_filter_level']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'safetySetting'],
+ getv(from_object, ['safety_filter_level']),
+ )
+
+ if getv(from_object, ['person_generation']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'personGeneration'],
+ getv(from_object, ['person_generation']),
+ )
+
+ if getv(from_object, ['include_safety_attributes']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'includeSafetyAttributes'],
+ getv(from_object, ['include_safety_attributes']),
+ )
+
+ if getv(from_object, ['include_rai_reason']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'includeRaiReason'],
+ getv(from_object, ['include_rai_reason']),
+ )
+
+ if getv(from_object, ['language']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'language'],
+ getv(from_object, ['language']),
+ )
+
+ if getv(from_object, ['output_mime_type']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'outputOptions', 'mimeType'],
+ getv(from_object, ['output_mime_type']),
+ )
+
+ if getv(from_object, ['output_compression_quality']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'outputOptions', 'compressionQuality'],
+ getv(from_object, ['output_compression_quality']),
+ )
+
+ if getv(from_object, ['add_watermark']) is not None:
+ raise ValueError('add_watermark parameter is not supported in Google AI.')
+
+ if getv(from_object, ['aspect_ratio']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'aspectRatio'],
+ getv(from_object, ['aspect_ratio']),
+ )
+
+ return to_object
+
+
+def _GenerateImageConfig_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, ['output_gcs_uri']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'storageUri'],
+ getv(from_object, ['output_gcs_uri']),
+ )
+
+ if getv(from_object, ['negative_prompt']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'negativePrompt'],
+ getv(from_object, ['negative_prompt']),
+ )
+
+ if getv(from_object, ['number_of_images']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'sampleCount'],
+ getv(from_object, ['number_of_images']),
+ )
+
+ if getv(from_object, ['guidance_scale']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'guidanceScale'],
+ getv(from_object, ['guidance_scale']),
+ )
+
+ if getv(from_object, ['seed']) is not None:
+ setv(parent_object, ['parameters', 'seed'], getv(from_object, ['seed']))
+
+ if getv(from_object, ['safety_filter_level']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'safetySetting'],
+ getv(from_object, ['safety_filter_level']),
+ )
+
+ if getv(from_object, ['person_generation']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'personGeneration'],
+ getv(from_object, ['person_generation']),
+ )
+
+ if getv(from_object, ['include_safety_attributes']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'includeSafetyAttributes'],
+ getv(from_object, ['include_safety_attributes']),
+ )
+
+ if getv(from_object, ['include_rai_reason']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'includeRaiReason'],
+ getv(from_object, ['include_rai_reason']),
+ )
+
+ if getv(from_object, ['language']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'language'],
+ getv(from_object, ['language']),
+ )
+
+ if getv(from_object, ['output_mime_type']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'outputOptions', 'mimeType'],
+ getv(from_object, ['output_mime_type']),
+ )
+
+ if getv(from_object, ['output_compression_quality']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'outputOptions', 'compressionQuality'],
+ getv(from_object, ['output_compression_quality']),
+ )
+
+ if getv(from_object, ['add_watermark']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'addWatermark'],
+ getv(from_object, ['add_watermark']),
+ )
+
+ if getv(from_object, ['aspect_ratio']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'aspectRatio'],
+ getv(from_object, ['aspect_ratio']),
+ )
+
+ return to_object
+
+
+def _GenerateImageParameters_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:
+ setv(
+ to_object,
+ ['_url', 'model'],
+ t.t_model(api_client, getv(from_object, ['model'])),
+ )
+
+ if getv(from_object, ['prompt']) is not None:
+ setv(to_object, ['instances', 'prompt'], getv(from_object, ['prompt']))
+
+ if getv(from_object, ['config']) is not None:
+ setv(
+ to_object,
+ ['config'],
+ _GenerateImageConfig_to_mldev(
+ api_client, getv(from_object, ['config']), to_object
+ ),
+ )
+
+ return to_object
+
+
+def _GenerateImageParameters_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,
+ ['_url', 'model'],
+ t.t_model(api_client, getv(from_object, ['model'])),
+ )
+
+ if getv(from_object, ['prompt']) is not None:
+ setv(to_object, ['instances', 'prompt'], getv(from_object, ['prompt']))
+
+ if getv(from_object, ['config']) is not None:
+ setv(
+ to_object,
+ ['config'],
+ _GenerateImageConfig_to_vertex(
+ api_client, getv(from_object, ['config']), to_object
+ ),
+ )
+
+ return to_object
+
+
+def _Image_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, ['image_bytes']) is not None:
+ setv(
+ to_object,
+ ['bytesBase64Encoded'],
+ t.t_bytes(api_client, getv(from_object, ['image_bytes'])),
+ )
+
+ return to_object
+
+
+def _Image_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, ['gcsUri'], getv(from_object, ['gcs_uri']))
+
+ if getv(from_object, ['image_bytes']) is not None:
+ setv(
+ to_object,
+ ['bytesBase64Encoded'],
+ t.t_bytes(api_client, getv(from_object, ['image_bytes'])),
+ )
+
+ return to_object
+
+
+def _MaskReferenceConfig_to_mldev(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['mask_mode']) is not None:
+ raise ValueError('mask_mode parameter is not supported in Google AI.')
+
+ if getv(from_object, ['segmentation_classes']) is not None:
+ raise ValueError(
+ 'segmentation_classes parameter is not supported in Google AI.'
+ )
+
+ if getv(from_object, ['mask_dilation']) is not None:
+ raise ValueError('mask_dilation parameter is not supported in Google AI.')
+
+ return to_object
+
+
+def _MaskReferenceConfig_to_vertex(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['mask_mode']) is not None:
+ setv(to_object, ['maskMode'], getv(from_object, ['mask_mode']))
+
+ if getv(from_object, ['segmentation_classes']) is not None:
+ setv(
+ to_object, ['maskClasses'], getv(from_object, ['segmentation_classes'])
+ )
+
+ if getv(from_object, ['mask_dilation']) is not None:
+ setv(to_object, ['dilation'], getv(from_object, ['mask_dilation']))
+
+ return to_object
+
+
+def _ControlReferenceConfig_to_mldev(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['control_type']) is not None:
+ raise ValueError('control_type parameter is not supported in Google AI.')
+
+ if getv(from_object, ['enable_control_image_computation']) is not None:
+ raise ValueError(
+ 'enable_control_image_computation parameter is not supported in'
+ ' Google AI.'
+ )
+
+ return to_object
+
+
+def _ControlReferenceConfig_to_vertex(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['control_type']) is not None:
+ setv(to_object, ['controlType'], getv(from_object, ['control_type']))
+
+ if getv(from_object, ['enable_control_image_computation']) is not None:
+ setv(
+ to_object,
+ ['computeControl'],
+ getv(from_object, ['enable_control_image_computation']),
+ )
+
+ return to_object
+
+
+def _StyleReferenceConfig_to_mldev(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['style_description']) is not None:
+ raise ValueError(
+ 'style_description parameter is not supported in Google AI.'
+ )
+
+ return to_object
+
+
+def _StyleReferenceConfig_to_vertex(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['style_description']) is not None:
+ setv(
+ to_object,
+ ['styleDescription'],
+ getv(from_object, ['style_description']),
+ )
+
+ return to_object
+
+
+def _SubjectReferenceConfig_to_mldev(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['subject_type']) is not None:
+ raise ValueError('subject_type parameter is not supported in Google AI.')
+
+ if getv(from_object, ['subject_description']) is not None:
+ raise ValueError(
+ 'subject_description parameter is not supported in Google AI.'
+ )
+
+ return to_object
+
+
+def _SubjectReferenceConfig_to_vertex(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['subject_type']) is not None:
+ setv(to_object, ['subjectType'], getv(from_object, ['subject_type']))
+
+ if getv(from_object, ['subject_description']) is not None:
+ setv(
+ to_object,
+ ['subjectDescription'],
+ getv(from_object, ['subject_description']),
+ )
+
+ return to_object
+
+
+def _ReferenceImageAPI_to_mldev(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['reference_image']) is not None:
+ raise ValueError('reference_image parameter is not supported in Google AI.')
+
+ if getv(from_object, ['reference_id']) is not None:
+ raise ValueError('reference_id parameter is not supported in Google AI.')
+
+ if getv(from_object, ['reference_type']) is not None:
+ raise ValueError('reference_type parameter is not supported in Google AI.')
+
+ if getv(from_object, ['mask_image_config']) is not None:
+ raise ValueError(
+ 'mask_image_config parameter is not supported in Google AI.'
+ )
+
+ if getv(from_object, ['control_image_config']) is not None:
+ raise ValueError(
+ 'control_image_config parameter is not supported in Google AI.'
+ )
+
+ if getv(from_object, ['style_image_config']) is not None:
+ raise ValueError(
+ 'style_image_config parameter is not supported in Google AI.'
+ )
+
+ if getv(from_object, ['subject_image_config']) is not None:
+ raise ValueError(
+ 'subject_image_config parameter is not supported in Google AI.'
+ )
+
+ return to_object
+
+
+def _ReferenceImageAPI_to_vertex(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['reference_image']) is not None:
+ setv(
+ to_object,
+ ['referenceImage'],
+ _Image_to_vertex(
+ api_client, getv(from_object, ['reference_image']), to_object
+ ),
+ )
+
+ if getv(from_object, ['reference_id']) is not None:
+ setv(to_object, ['referenceId'], getv(from_object, ['reference_id']))
+
+ if getv(from_object, ['reference_type']) is not None:
+ setv(to_object, ['referenceType'], getv(from_object, ['reference_type']))
+
+ if getv(from_object, ['mask_image_config']) is not None:
+ setv(
+ to_object,
+ ['maskImageConfig'],
+ _MaskReferenceConfig_to_vertex(
+ api_client, getv(from_object, ['mask_image_config']), to_object
+ ),
+ )
+
+ if getv(from_object, ['control_image_config']) is not None:
+ setv(
+ to_object,
+ ['controlImageConfig'],
+ _ControlReferenceConfig_to_vertex(
+ api_client, getv(from_object, ['control_image_config']), to_object
+ ),
+ )
+
+ if getv(from_object, ['style_image_config']) is not None:
+ setv(
+ to_object,
+ ['styleImageConfig'],
+ _StyleReferenceConfig_to_vertex(
+ api_client, getv(from_object, ['style_image_config']), to_object
+ ),
+ )
+
+ if getv(from_object, ['subject_image_config']) is not None:
+ setv(
+ to_object,
+ ['subjectImageConfig'],
+ _SubjectReferenceConfig_to_vertex(
+ api_client, getv(from_object, ['subject_image_config']), to_object
+ ),
+ )
+
+ return to_object
+
+
+def _EditImageConfig_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, ['output_gcs_uri']) is not None:
+ raise ValueError('output_gcs_uri parameter is not supported in Google AI.')
+
+ if getv(from_object, ['negative_prompt']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'negativePrompt'],
+ getv(from_object, ['negative_prompt']),
+ )
+
+ if getv(from_object, ['number_of_images']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'sampleCount'],
+ getv(from_object, ['number_of_images']),
+ )
+
+ if getv(from_object, ['guidance_scale']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'guidanceScale'],
+ getv(from_object, ['guidance_scale']),
+ )
+
+ if getv(from_object, ['seed']) is not None:
+ raise ValueError('seed parameter is not supported in Google AI.')
+
+ if getv(from_object, ['safety_filter_level']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'safetySetting'],
+ getv(from_object, ['safety_filter_level']),
+ )
+
+ if getv(from_object, ['person_generation']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'personGeneration'],
+ getv(from_object, ['person_generation']),
+ )
+
+ if getv(from_object, ['include_safety_attributes']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'includeSafetyAttributes'],
+ getv(from_object, ['include_safety_attributes']),
+ )
+
+ if getv(from_object, ['include_rai_reason']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'includeRaiReason'],
+ getv(from_object, ['include_rai_reason']),
+ )
+
+ if getv(from_object, ['language']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'language'],
+ getv(from_object, ['language']),
+ )
+
+ if getv(from_object, ['output_mime_type']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'outputOptions', 'mimeType'],
+ getv(from_object, ['output_mime_type']),
+ )
+
+ if getv(from_object, ['output_compression_quality']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'outputOptions', 'compressionQuality'],
+ getv(from_object, ['output_compression_quality']),
+ )
+
+ if getv(from_object, ['edit_mode']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'editMode'],
+ getv(from_object, ['edit_mode']),
+ )
+
+ return to_object
+
+
+def _EditImageConfig_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, ['output_gcs_uri']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'storageUri'],
+ getv(from_object, ['output_gcs_uri']),
+ )
+
+ if getv(from_object, ['negative_prompt']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'negativePrompt'],
+ getv(from_object, ['negative_prompt']),
+ )
+
+ if getv(from_object, ['number_of_images']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'sampleCount'],
+ getv(from_object, ['number_of_images']),
+ )
+
+ if getv(from_object, ['guidance_scale']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'guidanceScale'],
+ getv(from_object, ['guidance_scale']),
+ )
+
+ if getv(from_object, ['seed']) is not None:
+ setv(parent_object, ['parameters', 'seed'], getv(from_object, ['seed']))
+
+ if getv(from_object, ['safety_filter_level']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'safetySetting'],
+ getv(from_object, ['safety_filter_level']),
+ )
+
+ if getv(from_object, ['person_generation']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'personGeneration'],
+ getv(from_object, ['person_generation']),
+ )
+
+ if getv(from_object, ['include_safety_attributes']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'includeSafetyAttributes'],
+ getv(from_object, ['include_safety_attributes']),
+ )
+
+ if getv(from_object, ['include_rai_reason']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'includeRaiReason'],
+ getv(from_object, ['include_rai_reason']),
+ )
+
+ if getv(from_object, ['language']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'language'],
+ getv(from_object, ['language']),
+ )
+
+ if getv(from_object, ['output_mime_type']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'outputOptions', 'mimeType'],
+ getv(from_object, ['output_mime_type']),
+ )
+
+ if getv(from_object, ['output_compression_quality']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'outputOptions', 'compressionQuality'],
+ getv(from_object, ['output_compression_quality']),
+ )
+
+ if getv(from_object, ['edit_mode']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'editMode'],
+ getv(from_object, ['edit_mode']),
+ )
+
+ return to_object
+
+
+def _EditImageParameters_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:
+ setv(
+ to_object,
+ ['_url', 'model'],
+ t.t_model(api_client, getv(from_object, ['model'])),
+ )
+
+ if getv(from_object, ['prompt']) is not None:
+ setv(to_object, ['instances', 'prompt'], getv(from_object, ['prompt']))
+
+ if getv(from_object, ['reference_images']) is not None:
+ setv(
+ to_object,
+ ['instances', 'referenceImages'],
+ [
+ _ReferenceImageAPI_to_mldev(api_client, item, to_object)
+ for item in getv(from_object, ['reference_images'])
+ ],
+ )
+
+ if getv(from_object, ['config']) is not None:
+ setv(
+ to_object,
+ ['config'],
+ _EditImageConfig_to_mldev(
+ api_client, getv(from_object, ['config']), to_object
+ ),
+ )
+
+ return to_object
+
+
+def _EditImageParameters_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,
+ ['_url', 'model'],
+ t.t_model(api_client, getv(from_object, ['model'])),
+ )
+
+ if getv(from_object, ['prompt']) is not None:
+ setv(to_object, ['instances', 'prompt'], getv(from_object, ['prompt']))
+
+ if getv(from_object, ['reference_images']) is not None:
+ setv(
+ to_object,
+ ['instances', 'referenceImages'],
+ [
+ _ReferenceImageAPI_to_vertex(api_client, item, to_object)
+ for item in getv(from_object, ['reference_images'])
+ ],
+ )
+
+ if getv(from_object, ['config']) is not None:
+ setv(
+ to_object,
+ ['config'],
+ _EditImageConfig_to_vertex(
+ api_client, getv(from_object, ['config']), to_object
+ ),
+ )
+
+ return to_object
+
+
+def _UpscaleImageAPIConfig_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, ['include_rai_reason']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'includeRaiReason'],
+ getv(from_object, ['include_rai_reason']),
+ )
+
+ if getv(from_object, ['output_mime_type']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'outputOptions', 'mimeType'],
+ getv(from_object, ['output_mime_type']),
+ )
+
+ if getv(from_object, ['output_compression_quality']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'outputOptions', 'compressionQuality'],
+ getv(from_object, ['output_compression_quality']),
+ )
+
+ if getv(from_object, ['number_of_images']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'sampleCount'],
+ getv(from_object, ['number_of_images']),
+ )
+
+ if getv(from_object, ['mode']) is not None:
+ setv(parent_object, ['parameters', 'mode'], getv(from_object, ['mode']))
+
+ return to_object
+
+
+def _UpscaleImageAPIConfig_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, ['include_rai_reason']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'includeRaiReason'],
+ getv(from_object, ['include_rai_reason']),
+ )
+
+ if getv(from_object, ['output_mime_type']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'outputOptions', 'mimeType'],
+ getv(from_object, ['output_mime_type']),
+ )
+
+ if getv(from_object, ['output_compression_quality']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'outputOptions', 'compressionQuality'],
+ getv(from_object, ['output_compression_quality']),
+ )
+
+ if getv(from_object, ['number_of_images']) is not None:
+ setv(
+ parent_object,
+ ['parameters', 'sampleCount'],
+ getv(from_object, ['number_of_images']),
+ )
+
+ if getv(from_object, ['mode']) is not None:
+ setv(parent_object, ['parameters', 'mode'], getv(from_object, ['mode']))
+
+ return to_object
+
+
+def _UpscaleImageAPIParameters_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:
+ setv(
+ to_object,
+ ['_url', 'model'],
+ t.t_model(api_client, getv(from_object, ['model'])),
+ )
+
+ if getv(from_object, ['image']) is not None:
+ setv(
+ to_object,
+ ['instances', 'image'],
+ _Image_to_mldev(api_client, getv(from_object, ['image']), to_object),
+ )
+
+ if getv(from_object, ['upscale_factor']) is not None:
+ setv(
+ to_object,
+ ['parameters', 'upscaleConfig', 'upscaleFactor'],
+ getv(from_object, ['upscale_factor']),
+ )
+
+ if getv(from_object, ['config']) is not None:
+ setv(
+ to_object,
+ ['config'],
+ _UpscaleImageAPIConfig_to_mldev(
+ api_client, getv(from_object, ['config']), to_object
+ ),
+ )
+
+ return to_object
+
+
+def _UpscaleImageAPIParameters_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,
+ ['_url', 'model'],
+ t.t_model(api_client, getv(from_object, ['model'])),
+ )
+
+ if getv(from_object, ['image']) is not None:
+ setv(
+ to_object,
+ ['instances', 'image'],
+ _Image_to_vertex(api_client, getv(from_object, ['image']), to_object),
+ )
+
+ if getv(from_object, ['upscale_factor']) is not None:
+ setv(
+ to_object,
+ ['parameters', 'upscaleConfig', 'upscaleFactor'],
+ getv(from_object, ['upscale_factor']),
+ )
+
+ if getv(from_object, ['config']) is not None:
+ setv(
+ to_object,
+ ['config'],
+ _UpscaleImageAPIConfig_to_vertex(
+ api_client, getv(from_object, ['config']), to_object
+ ),
+ )
+
+ return to_object
+
+
+def _GetModelParameters_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:
+ setv(
+ to_object,
+ ['_url', 'name'],
+ t.t_model(api_client, getv(from_object, ['model'])),
+ )
+
+ return to_object
+
+
+def _GetModelParameters_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,
+ ['_url', 'name'],
+ t.t_model(api_client, getv(from_object, ['model'])),
+ )
+
+ return to_object
+
+
+def _ListModelsConfig_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:
+ setv(parent_object, ['_query', 'filter'], getv(from_object, ['filter']))
+
+ if getv(from_object, ['query_base']) is not None:
+ setv(
+ parent_object,
+ ['_url', 'models_url'],
+ t.t_models_url(api_client, getv(from_object, ['query_base'])),
+ )
+
+ return to_object
+
+
+def _ListModelsConfig_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']))
+
+ if getv(from_object, ['query_base']) is not None:
+ setv(
+ parent_object,
+ ['_url', 'models_url'],
+ t.t_models_url(api_client, getv(from_object, ['query_base'])),
+ )
+
+ return to_object
+
+
+def _ListModelsParameters_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'],
+ _ListModelsConfig_to_mldev(
+ api_client, getv(from_object, ['config']), to_object
+ ),
+ )
+
+ return to_object
+
+
+def _ListModelsParameters_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'],
+ _ListModelsConfig_to_vertex(
+ api_client, getv(from_object, ['config']), to_object
+ ),
+ )
+
+ return to_object
+
+
+def _UpdateModelConfig_to_mldev(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['display_name']) is not None:
+ setv(parent_object, ['displayName'], getv(from_object, ['display_name']))
+
+ if getv(from_object, ['description']) is not None:
+ setv(parent_object, ['description'], getv(from_object, ['description']))
+
+ return to_object
+
+
+def _UpdateModelConfig_to_vertex(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['display_name']) is not None:
+ setv(parent_object, ['displayName'], getv(from_object, ['display_name']))
+
+ if getv(from_object, ['description']) is not None:
+ setv(parent_object, ['description'], getv(from_object, ['description']))
+
+ return to_object
+
+
+def _UpdateModelParameters_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:
+ setv(
+ to_object,
+ ['_url', 'name'],
+ t.t_model(api_client, getv(from_object, ['model'])),
+ )
+
+ if getv(from_object, ['config']) is not None:
+ setv(
+ to_object,
+ ['config'],
+ _UpdateModelConfig_to_mldev(
+ api_client, getv(from_object, ['config']), to_object
+ ),
+ )
+
+ return to_object
+
+
+def _UpdateModelParameters_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,
+ ['_url', 'model'],
+ t.t_model(api_client, getv(from_object, ['model'])),
+ )
+
+ if getv(from_object, ['config']) is not None:
+ setv(
+ to_object,
+ ['config'],
+ _UpdateModelConfig_to_vertex(
+ api_client, getv(from_object, ['config']), to_object
+ ),
+ )
+
+ return to_object
+
+
+def _DeleteModelParameters_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:
+ setv(
+ to_object,
+ ['_url', 'name'],
+ t.t_model(api_client, getv(from_object, ['model'])),
+ )
+
+ return to_object
+
+
+def _DeleteModelParameters_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,
+ ['_url', 'name'],
+ t.t_model(api_client, getv(from_object, ['model'])),
+ )
+
+ return to_object
+
+
+def _CountTokensConfig_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, ['system_instruction']) is not None:
+ setv(
+ parent_object,
+ ['generateContentRequest', 'systemInstruction'],
+ _Content_to_mldev(
+ api_client,
+ t.t_content(api_client, getv(from_object, ['system_instruction'])),
+ to_object,
+ ),
+ )
+
+ if getv(from_object, ['tools']) is not None:
+ setv(
+ parent_object,
+ ['generateContentRequest', 'tools'],
+ [
+ _Tool_to_mldev(api_client, item, to_object)
+ for item in getv(from_object, ['tools'])
+ ],
+ )
+
+ if getv(from_object, ['generation_config']) is not None:
+ raise ValueError(
+ 'generation_config parameter is not supported in Google AI.'
+ )
+
+ return to_object
+
+
+def _CountTokensConfig_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, ['system_instruction']) is not None:
+ setv(
+ parent_object,
+ ['systemInstruction'],
+ _Content_to_vertex(
+ api_client,
+ t.t_content(api_client, getv(from_object, ['system_instruction'])),
+ to_object,
+ ),
+ )
+
+ if getv(from_object, ['tools']) is not None:
+ setv(
+ parent_object,
+ ['tools'],
+ [
+ _Tool_to_vertex(api_client, item, to_object)
+ for item in getv(from_object, ['tools'])
+ ],
+ )
+
+ if getv(from_object, ['generation_config']) is not None:
+ setv(
+ parent_object,
+ ['generationConfig'],
+ getv(from_object, ['generation_config']),
+ )
+
+ return to_object
+
+
+def _CountTokensParameters_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:
+ setv(
+ to_object,
+ ['_url', 'model'],
+ t.t_model(api_client, getv(from_object, ['model'])),
+ )
+
+ if getv(from_object, ['contents']) is not None:
+ setv(
+ to_object,
+ ['contents'],
+ [
+ _Content_to_mldev(api_client, item, to_object)
+ for item in t.t_contents(
+ api_client, getv(from_object, ['contents'])
+ )
+ ],
+ )
+
+ if getv(from_object, ['config']) is not None:
+ setv(
+ to_object,
+ ['config'],
+ _CountTokensConfig_to_mldev(
+ api_client, getv(from_object, ['config']), to_object
+ ),
+ )
+
+ return to_object
+
+
+def _CountTokensParameters_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,
+ ['_url', 'model'],
+ t.t_model(api_client, getv(from_object, ['model'])),
+ )
+
+ if getv(from_object, ['contents']) is not None:
+ setv(
+ to_object,
+ ['contents'],
+ [
+ _Content_to_vertex(api_client, item, to_object)
+ for item in t.t_contents(
+ api_client, getv(from_object, ['contents'])
+ )
+ ],
+ )
+
+ if getv(from_object, ['config']) is not None:
+ setv(
+ to_object,
+ ['config'],
+ _CountTokensConfig_to_vertex(
+ api_client, getv(from_object, ['config']), to_object
+ ),
+ )
+
+ return to_object
+
+
+def _ComputeTokensConfig_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 _ComputeTokensConfig_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 _ComputeTokensParameters_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:
+ setv(
+ to_object,
+ ['_url', 'model'],
+ t.t_model(api_client, getv(from_object, ['model'])),
+ )
+
+ if getv(from_object, ['contents']) is not None:
+ raise ValueError('contents parameter is not supported in Google AI.')
+
+ if getv(from_object, ['config']) is not None:
+ setv(
+ to_object,
+ ['config'],
+ _ComputeTokensConfig_to_mldev(
+ api_client, getv(from_object, ['config']), to_object
+ ),
+ )
+
+ return to_object
+
+
+def _ComputeTokensParameters_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,
+ ['_url', 'model'],
+ t.t_model(api_client, getv(from_object, ['model'])),
+ )
+
+ if getv(from_object, ['contents']) is not None:
+ setv(
+ to_object,
+ ['contents'],
+ [
+ _Content_to_vertex(api_client, item, to_object)
+ for item in t.t_contents(
+ api_client, getv(from_object, ['contents'])
+ )
+ ],
+ )
+
+ if getv(from_object, ['config']) is not None:
+ setv(
+ to_object,
+ ['config'],
+ _ComputeTokensConfig_to_vertex(
+ api_client, getv(from_object, ['config']), to_object
+ ),
+ )
+
+ return to_object
+
+
+def _Part_from_mldev(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+
+ if getv(from_object, ['thought']) is not None:
+ setv(to_object, ['thought'], getv(from_object, ['thought']))
+
+ if getv(from_object, ['codeExecutionResult']) is not None:
+ setv(
+ to_object,
+ ['code_execution_result'],
+ getv(from_object, ['codeExecutionResult']),
+ )
+
+ if getv(from_object, ['executableCode']) is not None:
+ setv(to_object, ['executable_code'], getv(from_object, ['executableCode']))
+
+ if getv(from_object, ['fileData']) is not None:
+ setv(to_object, ['file_data'], getv(from_object, ['fileData']))
+
+ if getv(from_object, ['functionCall']) is not None:
+ setv(to_object, ['function_call'], getv(from_object, ['functionCall']))
+
+ if getv(from_object, ['functionResponse']) is not None:
+ setv(
+ to_object,
+ ['function_response'],
+ getv(from_object, ['functionResponse']),
+ )
+
+ if getv(from_object, ['inlineData']) is not None:
+ setv(to_object, ['inline_data'], getv(from_object, ['inlineData']))
+
+ if getv(from_object, ['text']) is not None:
+ setv(to_object, ['text'], getv(from_object, ['text']))
+
+ return to_object
+
+
+def _Part_from_vertex(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['videoMetadata']) is not None:
+ setv(to_object, ['video_metadata'], getv(from_object, ['videoMetadata']))
+
+ if getv(from_object, ['thought']) is not None:
+ setv(to_object, ['thought'], getv(from_object, ['thought']))
+
+ if getv(from_object, ['codeExecutionResult']) is not None:
+ setv(
+ to_object,
+ ['code_execution_result'],
+ getv(from_object, ['codeExecutionResult']),
+ )
+
+ if getv(from_object, ['executableCode']) is not None:
+ setv(to_object, ['executable_code'], getv(from_object, ['executableCode']))
+
+ if getv(from_object, ['fileData']) is not None:
+ setv(to_object, ['file_data'], getv(from_object, ['fileData']))
+
+ if getv(from_object, ['functionCall']) is not None:
+ setv(to_object, ['function_call'], getv(from_object, ['functionCall']))
+
+ if getv(from_object, ['functionResponse']) is not None:
+ setv(
+ to_object,
+ ['function_response'],
+ getv(from_object, ['functionResponse']),
+ )
+
+ if getv(from_object, ['inlineData']) is not None:
+ setv(to_object, ['inline_data'], getv(from_object, ['inlineData']))
+
+ if getv(from_object, ['text']) is not None:
+ setv(to_object, ['text'], getv(from_object, ['text']))
+
+ return to_object
+
+
+def _Content_from_mldev(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['parts']) is not None:
+ setv(
+ to_object,
+ ['parts'],
+ [
+ _Part_from_mldev(api_client, item, to_object)
+ for item in getv(from_object, ['parts'])
+ ],
+ )
+
+ if getv(from_object, ['role']) is not None:
+ setv(to_object, ['role'], getv(from_object, ['role']))
+
+ return to_object
+
+
+def _Content_from_vertex(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['parts']) is not None:
+ setv(
+ to_object,
+ ['parts'],
+ [
+ _Part_from_vertex(api_client, item, to_object)
+ for item in getv(from_object, ['parts'])
+ ],
+ )
+
+ if getv(from_object, ['role']) is not None:
+ setv(to_object, ['role'], getv(from_object, ['role']))
+
+ return to_object
+
+
+def _CitationMetadata_from_mldev(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['citationSources']) is not None:
+ setv(to_object, ['citations'], getv(from_object, ['citationSources']))
+
+ return to_object
+
+
+def _CitationMetadata_from_vertex(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['citations']) is not None:
+ setv(to_object, ['citations'], getv(from_object, ['citations']))
+
+ return to_object
+
+
+def _Candidate_from_mldev(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['content']) is not None:
+ setv(
+ to_object,
+ ['content'],
+ _Content_from_mldev(
+ api_client, getv(from_object, ['content']), to_object
+ ),
+ )
+
+ if getv(from_object, ['citationMetadata']) is not None:
+ setv(
+ to_object,
+ ['citation_metadata'],
+ _CitationMetadata_from_mldev(
+ api_client, getv(from_object, ['citationMetadata']), to_object
+ ),
+ )
+
+ if getv(from_object, ['tokenCount']) is not None:
+ setv(to_object, ['token_count'], getv(from_object, ['tokenCount']))
+
+ if getv(from_object, ['avgLogprobs']) is not None:
+ setv(to_object, ['avg_logprobs'], getv(from_object, ['avgLogprobs']))
+
+ if getv(from_object, ['finishReason']) is not None:
+ setv(to_object, ['finish_reason'], getv(from_object, ['finishReason']))
+
+ if getv(from_object, ['groundingMetadata']) is not None:
+ setv(
+ to_object,
+ ['grounding_metadata'],
+ getv(from_object, ['groundingMetadata']),
+ )
+
+ if getv(from_object, ['index']) is not None:
+ setv(to_object, ['index'], getv(from_object, ['index']))
+
+ if getv(from_object, ['logprobsResult']) is not None:
+ setv(to_object, ['logprobs_result'], getv(from_object, ['logprobsResult']))
+
+ if getv(from_object, ['safetyRatings']) is not None:
+ setv(to_object, ['safety_ratings'], getv(from_object, ['safetyRatings']))
+
+ return to_object
+
+
+def _Candidate_from_vertex(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['content']) is not None:
+ setv(
+ to_object,
+ ['content'],
+ _Content_from_vertex(
+ api_client, getv(from_object, ['content']), to_object
+ ),
+ )
+
+ if getv(from_object, ['citationMetadata']) is not None:
+ setv(
+ to_object,
+ ['citation_metadata'],
+ _CitationMetadata_from_vertex(
+ api_client, getv(from_object, ['citationMetadata']), to_object
+ ),
+ )
+
+ if getv(from_object, ['finishMessage']) is not None:
+ setv(to_object, ['finish_message'], getv(from_object, ['finishMessage']))
+
+ if getv(from_object, ['avgLogprobs']) is not None:
+ setv(to_object, ['avg_logprobs'], getv(from_object, ['avgLogprobs']))
+
+ if getv(from_object, ['finishReason']) is not None:
+ setv(to_object, ['finish_reason'], getv(from_object, ['finishReason']))
+
+ if getv(from_object, ['groundingMetadata']) is not None:
+ setv(
+ to_object,
+ ['grounding_metadata'],
+ getv(from_object, ['groundingMetadata']),
+ )
+
+ if getv(from_object, ['index']) is not None:
+ setv(to_object, ['index'], getv(from_object, ['index']))
+
+ if getv(from_object, ['logprobsResult']) is not None:
+ setv(to_object, ['logprobs_result'], getv(from_object, ['logprobsResult']))
+
+ if getv(from_object, ['safetyRatings']) is not None:
+ setv(to_object, ['safety_ratings'], getv(from_object, ['safetyRatings']))
+
+ return to_object
+
+
+def _GenerateContentResponse_from_mldev(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['candidates']) is not None:
+ setv(
+ to_object,
+ ['candidates'],
+ [
+ _Candidate_from_mldev(api_client, item, to_object)
+ for item in getv(from_object, ['candidates'])
+ ],
+ )
+
+ if getv(from_object, ['modelVersion']) is not None:
+ setv(to_object, ['model_version'], getv(from_object, ['modelVersion']))
+
+ if getv(from_object, ['promptFeedback']) is not None:
+ setv(to_object, ['prompt_feedback'], getv(from_object, ['promptFeedback']))
+
+ if getv(from_object, ['usageMetadata']) is not None:
+ setv(to_object, ['usage_metadata'], getv(from_object, ['usageMetadata']))
+
+ return to_object
+
+
+def _GenerateContentResponse_from_vertex(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['candidates']) is not None:
+ setv(
+ to_object,
+ ['candidates'],
+ [
+ _Candidate_from_vertex(api_client, item, to_object)
+ for item in getv(from_object, ['candidates'])
+ ],
+ )
+
+ if getv(from_object, ['modelVersion']) is not None:
+ setv(to_object, ['model_version'], getv(from_object, ['modelVersion']))
+
+ if getv(from_object, ['promptFeedback']) is not None:
+ setv(to_object, ['prompt_feedback'], getv(from_object, ['promptFeedback']))
+
+ if getv(from_object, ['usageMetadata']) is not None:
+ setv(to_object, ['usage_metadata'], getv(from_object, ['usageMetadata']))
+
+ return to_object
+
+
+def _ContentEmbeddingStatistics_from_mldev(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+
+ return to_object
+
+
+def _ContentEmbeddingStatistics_from_vertex(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['truncated']) is not None:
+ setv(to_object, ['truncated'], getv(from_object, ['truncated']))
+
+ if getv(from_object, ['token_count']) is not None:
+ setv(to_object, ['token_count'], getv(from_object, ['token_count']))
+
+ return to_object
+
+
+def _ContentEmbedding_from_mldev(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['values']) is not None:
+ setv(to_object, ['values'], getv(from_object, ['values']))
+
+ return to_object
+
+
+def _ContentEmbedding_from_vertex(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['values']) is not None:
+ setv(to_object, ['values'], getv(from_object, ['values']))
+
+ if getv(from_object, ['statistics']) is not None:
+ setv(
+ to_object,
+ ['statistics'],
+ _ContentEmbeddingStatistics_from_vertex(
+ api_client, getv(from_object, ['statistics']), to_object
+ ),
+ )
+
+ return to_object
+
+
+def _EmbedContentMetadata_from_mldev(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+
+ return to_object
+
+
+def _EmbedContentMetadata_from_vertex(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['billableCharacterCount']) is not None:
+ setv(
+ to_object,
+ ['billable_character_count'],
+ getv(from_object, ['billableCharacterCount']),
+ )
+
+ return to_object
+
+
+def _EmbedContentResponse_from_mldev(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['embeddings']) is not None:
+ setv(
+ to_object,
+ ['embeddings'],
+ [
+ _ContentEmbedding_from_mldev(api_client, item, to_object)
+ for item in getv(from_object, ['embeddings'])
+ ],
+ )
+
+ if getv(from_object, ['metadata']) is not None:
+ setv(
+ to_object,
+ ['metadata'],
+ _EmbedContentMetadata_from_mldev(
+ api_client, getv(from_object, ['metadata']), to_object
+ ),
+ )
+
+ return to_object
+
+
+def _EmbedContentResponse_from_vertex(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['predictions[]', 'embeddings']) is not None:
+ setv(
+ to_object,
+ ['embeddings'],
+ [
+ _ContentEmbedding_from_vertex(api_client, item, to_object)
+ for item in getv(from_object, ['predictions[]', 'embeddings'])
+ ],
+ )
+
+ if getv(from_object, ['metadata']) is not None:
+ setv(
+ to_object,
+ ['metadata'],
+ _EmbedContentMetadata_from_vertex(
+ api_client, getv(from_object, ['metadata']), to_object
+ ),
+ )
+
+ return to_object
+
+
+def _Image_from_mldev(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+
+ if getv(from_object, ['bytesBase64Encoded']) is not None:
+ setv(
+ to_object,
+ ['image_bytes'],
+ t.t_bytes(api_client, getv(from_object, ['bytesBase64Encoded'])),
+ )
+
+ return to_object
+
+
+def _Image_from_vertex(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['gcsUri']) is not None:
+ setv(to_object, ['gcs_uri'], getv(from_object, ['gcsUri']))
+
+ if getv(from_object, ['bytesBase64Encoded']) is not None:
+ setv(
+ to_object,
+ ['image_bytes'],
+ t.t_bytes(api_client, getv(from_object, ['bytesBase64Encoded'])),
+ )
+
+ return to_object
+
+
+def _GeneratedImage_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,
+ ['image'],
+ _Image_from_mldev(api_client, getv(from_object, ['_self']), to_object),
+ )
+
+ if getv(from_object, ['raiFilteredReason']) is not None:
+ setv(
+ to_object,
+ ['rai_filtered_reason'],
+ getv(from_object, ['raiFilteredReason']),
+ )
+
+ return to_object
+
+
+def _GeneratedImage_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,
+ ['image'],
+ _Image_from_vertex(api_client, getv(from_object, ['_self']), to_object),
+ )
+
+ if getv(from_object, ['raiFilteredReason']) is not None:
+ setv(
+ to_object,
+ ['rai_filtered_reason'],
+ getv(from_object, ['raiFilteredReason']),
+ )
+
+ return to_object
+
+
+def _GenerateImageResponse_from_mldev(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['predictions']) is not None:
+ setv(
+ to_object,
+ ['generated_images'],
+ [
+ _GeneratedImage_from_mldev(api_client, item, to_object)
+ for item in getv(from_object, ['predictions'])
+ ],
+ )
+
+ return to_object
+
+
+def _GenerateImageResponse_from_vertex(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['predictions']) is not None:
+ setv(
+ to_object,
+ ['generated_images'],
+ [
+ _GeneratedImage_from_vertex(api_client, item, to_object)
+ for item in getv(from_object, ['predictions'])
+ ],
+ )
+
+ return to_object
+
+
+def _EditImageResponse_from_mldev(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['predictions']) is not None:
+ setv(
+ to_object,
+ ['generated_images'],
+ [
+ _GeneratedImage_from_mldev(api_client, item, to_object)
+ for item in getv(from_object, ['predictions'])
+ ],
+ )
+
+ return to_object
+
+
+def _EditImageResponse_from_vertex(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['predictions']) is not None:
+ setv(
+ to_object,
+ ['generated_images'],
+ [
+ _GeneratedImage_from_vertex(api_client, item, to_object)
+ for item in getv(from_object, ['predictions'])
+ ],
+ )
+
+ return to_object
+
+
+def _UpscaleImageResponse_from_mldev(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['predictions']) is not None:
+ setv(
+ to_object,
+ ['generated_images'],
+ [
+ _GeneratedImage_from_mldev(api_client, item, to_object)
+ for item in getv(from_object, ['predictions'])
+ ],
+ )
+
+ return to_object
+
+
+def _UpscaleImageResponse_from_vertex(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['predictions']) is not None:
+ setv(
+ to_object,
+ ['generated_images'],
+ [
+ _GeneratedImage_from_vertex(api_client, item, to_object)
+ for item in getv(from_object, ['predictions'])
+ ],
+ )
+
+ return to_object
+
+
+def _Endpoint_from_mldev(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+
+ return to_object
+
+
+def _Endpoint_from_vertex(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['endpoint']) is not None:
+ setv(to_object, ['name'], getv(from_object, ['endpoint']))
+
+ if getv(from_object, ['deployedModelId']) is not None:
+ setv(
+ to_object, ['deployed_model_id'], getv(from_object, ['deployedModelId'])
+ )
+
+ return to_object
+
+
+def _TunedModelInfo_from_mldev(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['baseModel']) is not None:
+ setv(to_object, ['base_model'], getv(from_object, ['baseModel']))
+
+ if getv(from_object, ['createTime']) is not None:
+ setv(to_object, ['create_time'], getv(from_object, ['createTime']))
+
+ if getv(from_object, ['updateTime']) is not None:
+ setv(to_object, ['update_time'], getv(from_object, ['updateTime']))
+
+ return to_object
+
+
+def _TunedModelInfo_from_vertex(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if (
+ getv(from_object, ['labels', 'google-vertex-llm-tuning-base-model-id'])
+ is not None
+ ):
+ setv(
+ to_object,
+ ['base_model'],
+ getv(from_object, ['labels', 'google-vertex-llm-tuning-base-model-id']),
+ )
+
+ if getv(from_object, ['createTime']) is not None:
+ setv(to_object, ['create_time'], getv(from_object, ['createTime']))
+
+ if getv(from_object, ['updateTime']) is not None:
+ setv(to_object, ['update_time'], getv(from_object, ['updateTime']))
+
+ return to_object
+
+
+def _Model_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, ['displayName']) is not None:
+ setv(to_object, ['display_name'], getv(from_object, ['displayName']))
+
+ if getv(from_object, ['description']) is not None:
+ setv(to_object, ['description'], getv(from_object, ['description']))
+
+ if getv(from_object, ['version']) is not None:
+ setv(to_object, ['version'], getv(from_object, ['version']))
+
+ if getv(from_object, ['_self']) is not None:
+ setv(
+ to_object,
+ ['tuned_model_info'],
+ _TunedModelInfo_from_mldev(
+ api_client, getv(from_object, ['_self']), to_object
+ ),
+ )
+
+ if getv(from_object, ['inputTokenLimit']) is not None:
+ setv(
+ to_object, ['input_token_limit'], getv(from_object, ['inputTokenLimit'])
+ )
+
+ if getv(from_object, ['outputTokenLimit']) is not None:
+ setv(
+ to_object,
+ ['output_token_limit'],
+ getv(from_object, ['outputTokenLimit']),
+ )
+
+ if getv(from_object, ['supportedGenerationMethods']) is not None:
+ setv(
+ to_object,
+ ['supported_actions'],
+ getv(from_object, ['supportedGenerationMethods']),
+ )
+
+ return to_object
+
+
+def _Model_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, ['description']) is not None:
+ setv(to_object, ['description'], getv(from_object, ['description']))
+
+ if getv(from_object, ['versionId']) is not None:
+ setv(to_object, ['version'], getv(from_object, ['versionId']))
+
+ if getv(from_object, ['deployedModels']) is not None:
+ setv(
+ to_object,
+ ['endpoints'],
+ [
+ _Endpoint_from_vertex(api_client, item, to_object)
+ for item in getv(from_object, ['deployedModels'])
+ ],
+ )
+
+ if getv(from_object, ['labels']) is not None:
+ setv(to_object, ['labels'], getv(from_object, ['labels']))
+
+ if getv(from_object, ['_self']) is not None:
+ setv(
+ to_object,
+ ['tuned_model_info'],
+ _TunedModelInfo_from_vertex(
+ api_client, getv(from_object, ['_self']), to_object
+ ),
+ )
+
+ return to_object
+
+
+def _ListModelsResponse_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, ['_self']) is not None:
+ setv(
+ to_object,
+ ['models'],
+ [
+ _Model_from_mldev(api_client, item, to_object)
+ for item in t.t_extract_models(
+ api_client, getv(from_object, ['_self'])
+ )
+ ],
+ )
+
+ return to_object
+
+
+def _ListModelsResponse_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, ['_self']) is not None:
+ setv(
+ to_object,
+ ['models'],
+ [
+ _Model_from_vertex(api_client, item, to_object)
+ for item in t.t_extract_models(
+ api_client, getv(from_object, ['_self'])
+ )
+ ],
+ )
+
+ return to_object
+
+
+def _DeleteModelResponse_from_mldev(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+
+ return to_object
+
+
+def _DeleteModelResponse_from_vertex(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+
+ return to_object
+
+
+def _CountTokensResponse_from_mldev(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['totalTokens']) is not None:
+ setv(to_object, ['total_tokens'], getv(from_object, ['totalTokens']))
+
+ if getv(from_object, ['cachedContentTokenCount']) is not None:
+ setv(
+ to_object,
+ ['cached_content_token_count'],
+ getv(from_object, ['cachedContentTokenCount']),
+ )
+
+ return to_object
+
+
+def _CountTokensResponse_from_vertex(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['totalTokens']) is not None:
+ setv(to_object, ['total_tokens'], getv(from_object, ['totalTokens']))
+
+ return to_object
+
+
+def _ComputeTokensResponse_from_mldev(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['tokensInfo']) is not None:
+ setv(to_object, ['tokens_info'], getv(from_object, ['tokensInfo']))
+
+ return to_object
+
+
+def _ComputeTokensResponse_from_vertex(
+ api_client: ApiClient,
+ from_object: Union[dict, object],
+ parent_object: dict = None,
+) -> dict:
+ to_object = {}
+ if getv(from_object, ['tokensInfo']) is not None:
+ setv(to_object, ['tokens_info'], getv(from_object, ['tokensInfo']))
+
+ return to_object
+
+
+class Models(_common.BaseModule):
+
+ def _generate_content(
+ self,
+ *,
+ model: str,
+ contents: Union[types.ContentListUnion, types.ContentListUnionDict],
+ config: Optional[types.GenerateContentConfigOrDict] = None,
+ ) -> types.GenerateContentResponse:
+ parameter_model = types._GenerateContentParameters(
+ model=model,
+ contents=contents,
+ config=config,
+ )
+
+ if self._api_client.vertexai:
+ request_dict = _GenerateContentParameters_to_vertex(
+ self._api_client, parameter_model
+ )
+ path = '{model}:generateContent'.format_map(request_dict.get('_url'))
+ else:
+ request_dict = _GenerateContentParameters_to_mldev(
+ self._api_client, parameter_model
+ )
+ path = '{model}:generateContent'.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 = _GenerateContentResponse_from_vertex(
+ self._api_client, response_dict
+ )
+ else:
+ response_dict = _GenerateContentResponse_from_mldev(
+ self._api_client, response_dict
+ )
+
+ return_value = types.GenerateContentResponse._from_response(
+ response_dict, parameter_model
+ )
+ self._api_client._verify_response(return_value)
+ return return_value
+
+ def generate_content_stream(
+ self,
+ *,
+ model: str,
+ contents: Union[types.ContentListUnion, types.ContentListUnionDict],
+ config: Optional[types.GenerateContentConfigOrDict] = None,
+ ) -> Iterator[types.GenerateContentResponse]:
+ parameter_model = types._GenerateContentParameters(
+ model=model,
+ contents=contents,
+ config=config,
+ )
+
+ if self._api_client.vertexai:
+ request_dict = _GenerateContentParameters_to_vertex(
+ self._api_client, parameter_model
+ )
+ path = '{model}:streamGenerateContent?alt=sse'.format_map(
+ request_dict.get('_url')
+ )
+ else:
+ request_dict = _GenerateContentParameters_to_mldev(
+ self._api_client, parameter_model
+ )
+ path = '{model}:streamGenerateContent?alt=sse'.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)
+
+ for response_dict in self._api_client.request_streamed(
+ 'post', path, request_dict, http_options
+ ):
+
+ if self._api_client.vertexai:
+ response_dict = _GenerateContentResponse_from_vertex(
+ self._api_client, response_dict
+ )
+ else:
+ response_dict = _GenerateContentResponse_from_mldev(
+ self._api_client, response_dict
+ )
+
+ return_value = types.GenerateContentResponse._from_response(
+ response_dict, parameter_model
+ )
+ self._api_client._verify_response(return_value)
+ yield return_value
+
+ def embed_content(
+ self,
+ *,
+ model: str,
+ contents: Union[types.ContentListUnion, types.ContentListUnionDict],
+ config: Optional[types.EmbedContentConfigOrDict] = None,
+ ) -> types.EmbedContentResponse:
+ """Calculates embeddings for the given contents(only text is supported).
+
+ Args:
+ model (str): The model to use.
+ contents (list[Content]): The contents to embed.
+ config (EmbedContentConfig): Optional configuration for embeddings.
+
+ Usage:
+
+ .. code-block:: python
+
+ embeddings = client.models.embed_content(
+ model= 'text-embedding-004',
+ contents=[
+ 'What is your name?',
+ 'What is your favorite color?',
+ ],
+ config={
+ 'output_dimensionality': 64
+ },
+ )
+ """
+
+ parameter_model = types._EmbedContentParameters(
+ model=model,
+ contents=contents,
+ config=config,
+ )
+
+ if self._api_client.vertexai:
+ request_dict = _EmbedContentParameters_to_vertex(
+ self._api_client, parameter_model
+ )
+ path = '{model}:predict'.format_map(request_dict.get('_url'))
+ else:
+ request_dict = _EmbedContentParameters_to_mldev(
+ self._api_client, parameter_model
+ )
+ path = '{model}:batchEmbedContents'.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 = _EmbedContentResponse_from_vertex(
+ self._api_client, response_dict
+ )
+ else:
+ response_dict = _EmbedContentResponse_from_mldev(
+ self._api_client, response_dict
+ )
+
+ return_value = types.EmbedContentResponse._from_response(
+ response_dict, parameter_model
+ )
+ self._api_client._verify_response(return_value)
+ return return_value
+
+ def generate_image(
+ self,
+ *,
+ model: str,
+ prompt: str,
+ config: Optional[types.GenerateImageConfigOrDict] = None,
+ ) -> types.GenerateImageResponse:
+ """Generates an image based on a text description and configuration.
+
+ Args:
+ model (str): The model to use.
+ prompt (str): A text description of the image to generate.
+ config (GenerateImageConfig): Configuration for generation.
+
+ Usage:
+
+ .. code-block:: python
+
+ response = client.models.generate_image(
+ model='imagen-3.0-generate-001',
+ prompt='Man with a dog',
+ config=types.GenerateImageConfig(
+ number_of_images= 1,
+ include_rai_reason= True,
+ )
+ )
+ response.generated_images[0].image.show()
+ # Shows a man with a dog.
+ """
+
+ parameter_model = types._GenerateImageParameters(
+ model=model,
+ prompt=prompt,
+ config=config,
+ )
+
+ if self._api_client.vertexai:
+ request_dict = _GenerateImageParameters_to_vertex(
+ self._api_client, parameter_model
+ )
+ path = '{model}:predict'.format_map(request_dict.get('_url'))
+ else:
+ request_dict = _GenerateImageParameters_to_mldev(
+ self._api_client, parameter_model
+ )
+ path = '{model}:predict'.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 = _GenerateImageResponse_from_vertex(
+ self._api_client, response_dict
+ )
+ else:
+ response_dict = _GenerateImageResponse_from_mldev(
+ self._api_client, response_dict
+ )
+
+ return_value = types.GenerateImageResponse._from_response(
+ response_dict, parameter_model
+ )
+ self._api_client._verify_response(return_value)
+ return return_value
+
+ def edit_image(
+ self,
+ *,
+ model: str,
+ prompt: str,
+ reference_images: list[types._ReferenceImageAPIOrDict],
+ config: Optional[types.EditImageConfigOrDict] = None,
+ ) -> types.EditImageResponse:
+ """Edits an image based on a text description and configuration.
+
+ Args:
+ model (str): The model to use.
+ prompt (str): A text description of the edit to apply to the image.
+ reference_images (list[Union[RawReferenceImage, MaskReferenceImage,
+ ControlReferenceImage, StyleReferenceImage, SubjectReferenceImage]): The
+ reference images for editing.
+ config (EditImageConfig): Configuration for editing.
+
+ Usage:
+
+ .. code-block:: python
+
+ from google.genai.types import RawReferenceImage, MaskReferenceImage
+
+ raw_ref_image = RawReferenceImage(
+ reference_id=1,
+ reference_image=types.Image.from_file(IMAGE_FILE_PATH),
+ )
+
+ mask_ref_image = MaskReferenceImage(
+ reference_id=2,
+ config=types.MaskReferenceConfig(
+ mask_mode='MASK_MODE_FOREGROUND',
+ mask_dilation=0.06,
+ ),
+ )
+ response = client.models.edit_image(
+ model='imagen-3.0-capability-preview-0930',
+ prompt='man with dog',
+ reference_images=[raw_ref_image, mask_ref_image],
+ config=types.EditImageConfig(
+ edit_mode= "EDIT_MODE_INPAINT_INSERTION",
+ number_of_images= 1,
+ include_rai_reason= True,
+ )
+ )
+ response.generated_images[0].image.show()
+ # Shows a man with a dog instead of a cat.
+ """
+
+ parameter_model = types._EditImageParameters(
+ model=model,
+ prompt=prompt,
+ reference_images=reference_images,
+ config=config,
+ )
+
+ if not self._api_client.vertexai:
+ raise ValueError('This method is only supported in the Vertex AI client.')
+ else:
+ request_dict = _EditImageParameters_to_vertex(
+ self._api_client, parameter_model
+ )
+ path = '{model}:predict'.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 = _EditImageResponse_from_vertex(
+ self._api_client, response_dict
+ )
+ else:
+ response_dict = _EditImageResponse_from_mldev(
+ self._api_client, response_dict
+ )
+
+ return_value = types.EditImageResponse._from_response(
+ response_dict, parameter_model
+ )
+ self._api_client._verify_response(return_value)
+ return return_value
+
+ def _upscale_image(
+ self,
+ *,
+ model: str,
+ image: types.ImageOrDict,
+ upscale_factor: str,
+ config: Optional[types._UpscaleImageAPIConfigOrDict] = None,
+ ) -> types.UpscaleImageResponse:
+ """Upscales an image.
+
+ Args:
+ model (str): The model to use.
+ image (Image): The input image for upscaling.
+ upscale_factor (str): The factor to upscale the image (x2 or x4).
+ config (_UpscaleImageAPIConfig): Configuration for upscaling.
+ """
+
+ parameter_model = types._UpscaleImageAPIParameters(
+ model=model,
+ image=image,
+ upscale_factor=upscale_factor,
+ config=config,
+ )
+
+ if not self._api_client.vertexai:
+ raise ValueError('This method is only supported in the Vertex AI client.')
+ else:
+ request_dict = _UpscaleImageAPIParameters_to_vertex(
+ self._api_client, parameter_model
+ )
+ path = '{model}:predict'.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 = _UpscaleImageResponse_from_vertex(
+ self._api_client, response_dict
+ )
+ else:
+ response_dict = _UpscaleImageResponse_from_mldev(
+ self._api_client, response_dict
+ )
+
+ return_value = types.UpscaleImageResponse._from_response(
+ response_dict, parameter_model
+ )
+ self._api_client._verify_response(return_value)
+ return return_value
+
+ def get(self, *, model: str) -> types.Model:
+ parameter_model = types._GetModelParameters(
+ model=model,
+ )
+
+ if self._api_client.vertexai:
+ request_dict = _GetModelParameters_to_vertex(
+ self._api_client, parameter_model
+ )
+ path = '{name}'.format_map(request_dict.get('_url'))
+ else:
+ request_dict = _GetModelParameters_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 = _Model_from_vertex(self._api_client, response_dict)
+ else:
+ response_dict = _Model_from_mldev(self._api_client, response_dict)
+
+ return_value = types.Model._from_response(response_dict, parameter_model)
+ self._api_client._verify_response(return_value)
+ return return_value
+
+ def _list(
+ self, *, config: Optional[types.ListModelsConfigOrDict] = None
+ ) -> types.ListModelsResponse:
+ parameter_model = types._ListModelsParameters(
+ config=config,
+ )
+
+ if self._api_client.vertexai:
+ request_dict = _ListModelsParameters_to_vertex(
+ self._api_client, parameter_model
+ )
+ path = '{models_url}'.format_map(request_dict.get('_url'))
+ else:
+ request_dict = _ListModelsParameters_to_mldev(
+ self._api_client, parameter_model
+ )
+ path = '{models_url}'.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 = _ListModelsResponse_from_vertex(
+ self._api_client, response_dict
+ )
+ else:
+ response_dict = _ListModelsResponse_from_mldev(
+ self._api_client, response_dict
+ )
+
+ return_value = types.ListModelsResponse._from_response(
+ response_dict, parameter_model
+ )
+ self._api_client._verify_response(return_value)
+ return return_value
+
+ def update(
+ self,
+ *,
+ model: str,
+ config: Optional[types.UpdateModelConfigOrDict] = None,
+ ) -> types.Model:
+ parameter_model = types._UpdateModelParameters(
+ model=model,
+ config=config,
+ )
+
+ if self._api_client.vertexai:
+ request_dict = _UpdateModelParameters_to_vertex(
+ self._api_client, parameter_model
+ )
+ path = '{model}'.format_map(request_dict.get('_url'))
+ else:
+ request_dict = _UpdateModelParameters_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(
+ 'patch', path, request_dict, http_options
+ )
+
+ if self._api_client.vertexai:
+ response_dict = _Model_from_vertex(self._api_client, response_dict)
+ else:
+ response_dict = _Model_from_mldev(self._api_client, response_dict)
+
+ return_value = types.Model._from_response(response_dict, parameter_model)
+ self._api_client._verify_response(return_value)
+ return return_value
+
+ def delete(self, *, model: str) -> types.DeleteModelResponse:
+ parameter_model = types._DeleteModelParameters(
+ model=model,
+ )
+
+ if self._api_client.vertexai:
+ request_dict = _DeleteModelParameters_to_vertex(
+ self._api_client, parameter_model
+ )
+ path = '{name}'.format_map(request_dict.get('_url'))
+ else:
+ request_dict = _DeleteModelParameters_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(
+ 'delete', path, request_dict, http_options
+ )
+
+ if self._api_client.vertexai:
+ response_dict = _DeleteModelResponse_from_vertex(
+ self._api_client, response_dict
+ )
+ else:
+ response_dict = _DeleteModelResponse_from_mldev(
+ self._api_client, response_dict
+ )
+
+ return_value = types.DeleteModelResponse._from_response(
+ response_dict, parameter_model
+ )
+ self._api_client._verify_response(return_value)
+ return return_value
+
+ def count_tokens(
+ self,
+ *,
+ model: str,
+ contents: Union[types.ContentListUnion, types.ContentListUnionDict],
+ config: Optional[types.CountTokensConfigOrDict] = None,
+ ) -> types.CountTokensResponse:
+ """Counts the number of tokens in the given content.
+
+ Args:
+ model (str): The model to use for counting tokens.
+ contents (list[types.Content]): The content to count tokens for.
+ Multimodal input is supported for Gemini models.
+ config (CountTokensConfig): The configuration for counting tokens.
+
+ Usage:
+
+ .. code-block:: python
+
+ response = client.models.count_tokens(
+ model='gemini-1.5-flash',
+ contents='What is your name?',
+ )
+ print(response)
+ # total_tokens=5 cached_content_token_count=None
+ """
+
+ parameter_model = types._CountTokensParameters(
+ model=model,
+ contents=contents,
+ config=config,
+ )
+
+ if self._api_client.vertexai:
+ request_dict = _CountTokensParameters_to_vertex(
+ self._api_client, parameter_model
+ )
+ path = '{model}:countTokens'.format_map(request_dict.get('_url'))
+ else:
+ request_dict = _CountTokensParameters_to_mldev(
+ self._api_client, parameter_model
+ )
+ path = '{model}:countTokens'.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 = _CountTokensResponse_from_vertex(
+ self._api_client, response_dict
+ )
+ else:
+ response_dict = _CountTokensResponse_from_mldev(
+ self._api_client, response_dict
+ )
+
+ return_value = types.CountTokensResponse._from_response(
+ response_dict, parameter_model
+ )
+ self._api_client._verify_response(return_value)
+ return return_value
+
+ def compute_tokens(
+ self,
+ *,
+ model: str,
+ contents: Union[types.ContentListUnion, types.ContentListUnionDict],
+ config: Optional[types.ComputeTokensConfigOrDict] = None,
+ ) -> types.ComputeTokensResponse:
+ """Return a list of tokens based on the input text.
+
+ This method is not supported by the Gemini Developer API.
+
+ Args:
+ model (str): The model to use.
+ contents (list[shared.Content]): The content to compute tokens for. Only
+ text is supported.
+
+ Usage:
+
+ .. code-block:: python
+
+ response = client.models.compute_tokens(
+ model='gemini-1.5-flash',
+ contents='What is your name?',
+ )
+ print(response)
+ # tokens_info=[TokensInfo(role='user', token_ids=['1841', ...],
+ # tokens=[b'What', b' is', b' your', b' name', b'?'])]
+ """
+
+ parameter_model = types._ComputeTokensParameters(
+ model=model,
+ contents=contents,
+ config=config,
+ )
+
+ if not self._api_client.vertexai:
+ raise ValueError('This method is only supported in the Vertex AI client.')
+ else:
+ request_dict = _ComputeTokensParameters_to_vertex(
+ self._api_client, parameter_model
+ )
+ path = '{model}:computeTokens'.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 = _ComputeTokensResponse_from_vertex(
+ self._api_client, response_dict
+ )
+ else:
+ response_dict = _ComputeTokensResponse_from_mldev(
+ self._api_client, response_dict
+ )
+
+ return_value = types.ComputeTokensResponse._from_response(
+ response_dict, parameter_model
+ )
+ self._api_client._verify_response(return_value)
+ return return_value
+
+ def generate_content(
+ self,
+ *,
+ model: str,
+ contents: Union[types.ContentListUnion, types.ContentListUnionDict],
+ config: Optional[types.GenerateContentConfigOrDict] = None,
+ ) -> types.GenerateContentResponse:
+ """Makes an API request to generate content using a model.
+
+ Some models support multimodal input and output.
+
+ Usage:
+
+ .. code-block:: python
+
+ from google.genai import types
+ from google import genai
+
+ client = genai.Client(
+ vertexai=True, project='my-project-id', location='us-central1'
+ )
+
+ response = client.models.generate_content(
+ model='gemini-1.5-flash-002',
+ contents='''What is a good name for a flower shop that specializes in
+ selling bouquets of dried flowers?'''
+ )
+ print(response.text)
+ # **Elegant & Classic:**
+ # * The Dried Bloom
+ # * Everlasting Florals
+ # * Timeless Petals
+
+ response = client.models.generate_content(
+ model='gemini-1.5-flash-002',
+ contents=[
+ types.Part.from_text('What is shown in this image?'),
+ types.Part.from_uri('gs://generativeai-downloads/images/scones.jpg',
+ 'image/jpeg')
+ ]
+ )
+ print(response.text)
+ # The image shows a flat lay arrangement of freshly baked blueberry
+ # scones.
+ """
+
+ if _extra_utils.should_disable_afc(config):
+ return self._generate_content(
+ model=model, contents=contents, config=config
+ )
+ remaining_remote_calls_afc = _extra_utils.get_max_remote_calls_afc(config)
+ logging.info(
+ f'AFC is enabled with max remote calls: {remaining_remote_calls_afc}.'
+ )
+ automatic_function_calling_history = []
+ while remaining_remote_calls_afc > 0:
+ response = self._generate_content(
+ model=model, contents=contents, config=config
+ )
+ remaining_remote_calls_afc -= 1
+ if remaining_remote_calls_afc == 0:
+ logging.info('Reached max remote calls for automatic function calling.')
+
+ function_map = _extra_utils.get_function_map(config)
+ if not function_map:
+ break
+ if (
+ not response.candidates
+ or not response.candidates[0].content
+ or not response.candidates[0].content.parts
+ ):
+ break
+ func_response_parts = _extra_utils.get_function_response_parts(
+ response, function_map
+ )
+ if not func_response_parts:
+ break
+ contents = t.t_contents(self._api_client, contents)
+ contents.append(response.candidates[0].content)
+ contents.append(
+ types.Content(
+ role='user',
+ parts=func_response_parts,
+ )
+ )
+ automatic_function_calling_history.extend(contents)
+ if _extra_utils.should_append_afc_history(config):
+ response.automatic_function_calling_history = (
+ automatic_function_calling_history
+ )
+ return response
+
+ def upscale_image(
+ self,
+ *,
+ model: str,
+ image: types.ImageOrDict,
+ upscale_factor: str,
+ config: Optional[types.UpscaleImageConfigOrDict] = None,
+ ) -> types.UpscaleImageResponse:
+ """Makes an API request to upscale a provided image.
+
+ Args:
+ model (str): The model to use.
+ image (Image): The input image for upscaling.
+ upscale_factor (str): The factor to upscale the image (x2 or x4).
+ config (UpscaleImageConfig): Configuration for upscaling.
+
+ Usage:
+
+ .. code-block:: python
+
+ from google.genai.types import Image
+
+ IMAGE_FILE_PATH="my-image.png"
+ response=client.models.upscale_image(
+ model='imagen-3.0-generate-001',
+ image=types.Image.from_file(IMAGE_FILE_PATH),
+ upscale_factor='x2',
+ )
+ response.generated_images[0].image.show()
+ # Opens my-image.png which is upscaled by a factor of 2.
+ """
+
+ # Validate config.
+ types.UpscaleImageParameters(
+ model=model,
+ image=image,
+ upscale_factor=upscale_factor,
+ config=config,
+ )
+
+ # Convert to API config.
+ config = config or {}
+ config_dct = config if isinstance(config, dict) else config.dict()
+ api_config = types._UpscaleImageAPIConfigDict(**config_dct) # pylint: disable=protected-access
+
+ # Provide default values through API config.
+ api_config['mode'] = 'upscale'
+ api_config['number_of_images'] = 1
+
+ return self._upscale_image(
+ model=model,
+ image=image,
+ upscale_factor=upscale_factor,
+ config=api_config,
+ )
+
+ def list(
+ self,
+ *,
+ config: Optional[types.ListModelsConfigOrDict] = None,
+ ) -> Pager[types.Model]:
+ """Makes an API request to list the available models.
+
+ If `query_base` is set to True in the config, the API will return all
+ available base models. If set to False or not set (default), it will return
+ all tuned models.
+
+ Args:
+ config (ListModelsConfigOrDict): Configuration for retrieving models.
+
+ Usage:
+
+ .. code-block:: python
+
+ response=client.models.list(config={'page_size': 5})
+ print(response.page)
+ # [Model(name='projects/./locations/./models/123', display_name='my_model'
+
+ response=client.models.list(config={'page_size': 5, 'query_base': True})
+ print(response.page)
+ # [Model(name='publishers/google/models/gemini-2.0-flash-exp' ...
+ """
+
+ config = (
+ types._ListModelsParameters(config=config).config
+ or types.ListModelsConfig()
+ )
+ if self._api_client.vertexai:
+ config = config.copy()
+ if config.query_base:
+ http_options = (
+ config.http_options if config.http_options else HttpOptionsDict()
+ )
+ http_options['skip_project_and_location_in_path'] = True
+ config.http_options = http_options
+ else:
+ # Filter for tuning jobs artifacts by labels.
+ filter_value = config.filter
+ config.filter = (
+ filter_value + '&filter=labels.tune-type:*'
+ if filter_value
+ else 'labels.tune-type:*'
+ )
+ if not config.query_base:
+ config.query_base = False
+ return Pager(
+ 'models',
+ self._list,
+ self._list(config=config),
+ config,
+ )
+
+
+class AsyncModels(_common.BaseModule):
+
+ async def _generate_content(
+ self,
+ *,
+ model: str,
+ contents: Union[types.ContentListUnion, types.ContentListUnionDict],
+ config: Optional[types.GenerateContentConfigOrDict] = None,
+ ) -> types.GenerateContentResponse:
+ parameter_model = types._GenerateContentParameters(
+ model=model,
+ contents=contents,
+ config=config,
+ )
+
+ if self._api_client.vertexai:
+ request_dict = _GenerateContentParameters_to_vertex(
+ self._api_client, parameter_model
+ )
+ path = '{model}:generateContent'.format_map(request_dict.get('_url'))
+ else:
+ request_dict = _GenerateContentParameters_to_mldev(
+ self._api_client, parameter_model
+ )
+ path = '{model}:generateContent'.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 = _GenerateContentResponse_from_vertex(
+ self._api_client, response_dict
+ )
+ else:
+ response_dict = _GenerateContentResponse_from_mldev(
+ self._api_client, response_dict
+ )
+
+ return_value = types.GenerateContentResponse._from_response(
+ response_dict, parameter_model
+ )
+ self._api_client._verify_response(return_value)
+ return return_value
+
+ async def generate_content_stream(
+ self,
+ *,
+ model: str,
+ contents: Union[types.ContentListUnion, types.ContentListUnionDict],
+ config: Optional[types.GenerateContentConfigOrDict] = None,
+ ) -> AsyncIterator[types.GenerateContentResponse]:
+ parameter_model = types._GenerateContentParameters(
+ model=model,
+ contents=contents,
+ config=config,
+ )
+
+ if self._api_client.vertexai:
+ request_dict = _GenerateContentParameters_to_vertex(
+ self._api_client, parameter_model
+ )
+ path = '{model}:streamGenerateContent?alt=sse'.format_map(
+ request_dict.get('_url')
+ )
+ else:
+ request_dict = _GenerateContentParameters_to_mldev(
+ self._api_client, parameter_model
+ )
+ path = '{model}:streamGenerateContent?alt=sse'.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)
+
+ async for response_dict in self._api_client.async_request_streamed(
+ 'post', path, request_dict, http_options
+ ):
+
+ if self._api_client.vertexai:
+ response_dict = _GenerateContentResponse_from_vertex(
+ self._api_client, response_dict
+ )
+ else:
+ response_dict = _GenerateContentResponse_from_mldev(
+ self._api_client, response_dict
+ )
+
+ return_value = types.GenerateContentResponse._from_response(
+ response_dict, parameter_model
+ )
+ self._api_client._verify_response(return_value)
+ yield return_value
+
+ async def embed_content(
+ self,
+ *,
+ model: str,
+ contents: Union[types.ContentListUnion, types.ContentListUnionDict],
+ config: Optional[types.EmbedContentConfigOrDict] = None,
+ ) -> types.EmbedContentResponse:
+ """Calculates embeddings for the given contents(only text is supported).
+
+ Args:
+ model (str): The model to use.
+ contents (list[Content]): The contents to embed.
+ config (EmbedContentConfig): Optional configuration for embeddings.
+
+ Usage:
+
+ .. code-block:: python
+
+ embeddings = client.models.embed_content(
+ model= 'text-embedding-004',
+ contents=[
+ 'What is your name?',
+ 'What is your favorite color?',
+ ],
+ config={
+ 'output_dimensionality': 64
+ },
+ )
+ """
+
+ parameter_model = types._EmbedContentParameters(
+ model=model,
+ contents=contents,
+ config=config,
+ )
+
+ if self._api_client.vertexai:
+ request_dict = _EmbedContentParameters_to_vertex(
+ self._api_client, parameter_model
+ )
+ path = '{model}:predict'.format_map(request_dict.get('_url'))
+ else:
+ request_dict = _EmbedContentParameters_to_mldev(
+ self._api_client, parameter_model
+ )
+ path = '{model}:batchEmbedContents'.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 = _EmbedContentResponse_from_vertex(
+ self._api_client, response_dict
+ )
+ else:
+ response_dict = _EmbedContentResponse_from_mldev(
+ self._api_client, response_dict
+ )
+
+ return_value = types.EmbedContentResponse._from_response(
+ response_dict, parameter_model
+ )
+ self._api_client._verify_response(return_value)
+ return return_value
+
+ async def generate_image(
+ self,
+ *,
+ model: str,
+ prompt: str,
+ config: Optional[types.GenerateImageConfigOrDict] = None,
+ ) -> types.GenerateImageResponse:
+ """Generates an image based on a text description and configuration.
+
+ Args:
+ model (str): The model to use.
+ prompt (str): A text description of the image to generate.
+ config (GenerateImageConfig): Configuration for generation.
+
+ Usage:
+
+ .. code-block:: python
+
+ response = client.models.generate_image(
+ model='imagen-3.0-generate-001',
+ prompt='Man with a dog',
+ config=types.GenerateImageConfig(
+ number_of_images= 1,
+ include_rai_reason= True,
+ )
+ )
+ response.generated_images[0].image.show()
+ # Shows a man with a dog.
+ """
+
+ parameter_model = types._GenerateImageParameters(
+ model=model,
+ prompt=prompt,
+ config=config,
+ )
+
+ if self._api_client.vertexai:
+ request_dict = _GenerateImageParameters_to_vertex(
+ self._api_client, parameter_model
+ )
+ path = '{model}:predict'.format_map(request_dict.get('_url'))
+ else:
+ request_dict = _GenerateImageParameters_to_mldev(
+ self._api_client, parameter_model
+ )
+ path = '{model}:predict'.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 = _GenerateImageResponse_from_vertex(
+ self._api_client, response_dict
+ )
+ else:
+ response_dict = _GenerateImageResponse_from_mldev(
+ self._api_client, response_dict
+ )
+
+ return_value = types.GenerateImageResponse._from_response(
+ response_dict, parameter_model
+ )
+ self._api_client._verify_response(return_value)
+ return return_value
+
+ async def edit_image(
+ self,
+ *,
+ model: str,
+ prompt: str,
+ reference_images: list[types._ReferenceImageAPIOrDict],
+ config: Optional[types.EditImageConfigOrDict] = None,
+ ) -> types.EditImageResponse:
+ """Edits an image based on a text description and configuration.
+
+ Args:
+ model (str): The model to use.
+ prompt (str): A text description of the edit to apply to the image.
+ reference_images (list[Union[RawReferenceImage, MaskReferenceImage,
+ ControlReferenceImage, StyleReferenceImage, SubjectReferenceImage]): The
+ reference images for editing.
+ config (EditImageConfig): Configuration for editing.
+
+ Usage:
+
+ .. code-block:: python
+
+ from google.genai.types import RawReferenceImage, MaskReferenceImage
+
+ raw_ref_image = RawReferenceImage(
+ reference_id=1,
+ reference_image=types.Image.from_file(IMAGE_FILE_PATH),
+ )
+
+ mask_ref_image = MaskReferenceImage(
+ reference_id=2,
+ config=types.MaskReferenceConfig(
+ mask_mode='MASK_MODE_FOREGROUND',
+ mask_dilation=0.06,
+ ),
+ )
+ response = client.models.edit_image(
+ model='imagen-3.0-capability-preview-0930',
+ prompt='man with dog',
+ reference_images=[raw_ref_image, mask_ref_image],
+ config=types.EditImageConfig(
+ edit_mode= "EDIT_MODE_INPAINT_INSERTION",
+ number_of_images= 1,
+ include_rai_reason= True,
+ )
+ )
+ response.generated_images[0].image.show()
+ # Shows a man with a dog instead of a cat.
+ """
+
+ parameter_model = types._EditImageParameters(
+ model=model,
+ prompt=prompt,
+ reference_images=reference_images,
+ config=config,
+ )
+
+ if not self._api_client.vertexai:
+ raise ValueError('This method is only supported in the Vertex AI client.')
+ else:
+ request_dict = _EditImageParameters_to_vertex(
+ self._api_client, parameter_model
+ )
+ path = '{model}:predict'.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 = _EditImageResponse_from_vertex(
+ self._api_client, response_dict
+ )
+ else:
+ response_dict = _EditImageResponse_from_mldev(
+ self._api_client, response_dict
+ )
+
+ return_value = types.EditImageResponse._from_response(
+ response_dict, parameter_model
+ )
+ self._api_client._verify_response(return_value)
+ return return_value
+
+ async def _upscale_image(
+ self,
+ *,
+ model: str,
+ image: types.ImageOrDict,
+ upscale_factor: str,
+ config: Optional[types._UpscaleImageAPIConfigOrDict] = None,
+ ) -> types.UpscaleImageResponse:
+ """Upscales an image.
+
+ Args:
+ model (str): The model to use.
+ image (Image): The input image for upscaling.
+ upscale_factor (str): The factor to upscale the image (x2 or x4).
+ config (_UpscaleImageAPIConfig): Configuration for upscaling.
+ """
+
+ parameter_model = types._UpscaleImageAPIParameters(
+ model=model,
+ image=image,
+ upscale_factor=upscale_factor,
+ config=config,
+ )
+
+ if not self._api_client.vertexai:
+ raise ValueError('This method is only supported in the Vertex AI client.')
+ else:
+ request_dict = _UpscaleImageAPIParameters_to_vertex(
+ self._api_client, parameter_model
+ )
+ path = '{model}:predict'.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 = _UpscaleImageResponse_from_vertex(
+ self._api_client, response_dict
+ )
+ else:
+ response_dict = _UpscaleImageResponse_from_mldev(
+ self._api_client, response_dict
+ )
+
+ return_value = types.UpscaleImageResponse._from_response(
+ response_dict, parameter_model
+ )
+ self._api_client._verify_response(return_value)
+ return return_value
+
+ async def get(self, *, model: str) -> types.Model:
+ parameter_model = types._GetModelParameters(
+ model=model,
+ )
+
+ if self._api_client.vertexai:
+ request_dict = _GetModelParameters_to_vertex(
+ self._api_client, parameter_model
+ )
+ path = '{name}'.format_map(request_dict.get('_url'))
+ else:
+ request_dict = _GetModelParameters_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 = _Model_from_vertex(self._api_client, response_dict)
+ else:
+ response_dict = _Model_from_mldev(self._api_client, response_dict)
+
+ return_value = types.Model._from_response(response_dict, parameter_model)
+ self._api_client._verify_response(return_value)
+ return return_value
+
+ async def _list(
+ self, *, config: Optional[types.ListModelsConfigOrDict] = None
+ ) -> types.ListModelsResponse:
+ parameter_model = types._ListModelsParameters(
+ config=config,
+ )
+
+ if self._api_client.vertexai:
+ request_dict = _ListModelsParameters_to_vertex(
+ self._api_client, parameter_model
+ )
+ path = '{models_url}'.format_map(request_dict.get('_url'))
+ else:
+ request_dict = _ListModelsParameters_to_mldev(
+ self._api_client, parameter_model
+ )
+ path = '{models_url}'.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 = _ListModelsResponse_from_vertex(
+ self._api_client, response_dict
+ )
+ else:
+ response_dict = _ListModelsResponse_from_mldev(
+ self._api_client, response_dict
+ )
+
+ return_value = types.ListModelsResponse._from_response(
+ response_dict, parameter_model
+ )
+ self._api_client._verify_response(return_value)
+ return return_value
+
+ async def update(
+ self,
+ *,
+ model: str,
+ config: Optional[types.UpdateModelConfigOrDict] = None,
+ ) -> types.Model:
+ parameter_model = types._UpdateModelParameters(
+ model=model,
+ config=config,
+ )
+
+ if self._api_client.vertexai:
+ request_dict = _UpdateModelParameters_to_vertex(
+ self._api_client, parameter_model
+ )
+ path = '{model}'.format_map(request_dict.get('_url'))
+ else:
+ request_dict = _UpdateModelParameters_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(
+ 'patch', path, request_dict, http_options
+ )
+
+ if self._api_client.vertexai:
+ response_dict = _Model_from_vertex(self._api_client, response_dict)
+ else:
+ response_dict = _Model_from_mldev(self._api_client, response_dict)
+
+ return_value = types.Model._from_response(response_dict, parameter_model)
+ self._api_client._verify_response(return_value)
+ return return_value
+
+ async def delete(self, *, model: str) -> types.DeleteModelResponse:
+ parameter_model = types._DeleteModelParameters(
+ model=model,
+ )
+
+ if self._api_client.vertexai:
+ request_dict = _DeleteModelParameters_to_vertex(
+ self._api_client, parameter_model
+ )
+ path = '{name}'.format_map(request_dict.get('_url'))
+ else:
+ request_dict = _DeleteModelParameters_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(
+ 'delete', path, request_dict, http_options
+ )
+
+ if self._api_client.vertexai:
+ response_dict = _DeleteModelResponse_from_vertex(
+ self._api_client, response_dict
+ )
+ else:
+ response_dict = _DeleteModelResponse_from_mldev(
+ self._api_client, response_dict
+ )
+
+ return_value = types.DeleteModelResponse._from_response(
+ response_dict, parameter_model
+ )
+ self._api_client._verify_response(return_value)
+ return return_value
+
+ async def count_tokens(
+ self,
+ *,
+ model: str,
+ contents: Union[types.ContentListUnion, types.ContentListUnionDict],
+ config: Optional[types.CountTokensConfigOrDict] = None,
+ ) -> types.CountTokensResponse:
+ """Counts the number of tokens in the given content.
+
+ Args:
+ model (str): The model to use for counting tokens.
+ contents (list[types.Content]): The content to count tokens for.
+ Multimodal input is supported for Gemini models.
+ config (CountTokensConfig): The configuration for counting tokens.
+
+ Usage:
+
+ .. code-block:: python
+
+ response = client.models.count_tokens(
+ model='gemini-1.5-flash',
+ contents='What is your name?',
+ )
+ print(response)
+ # total_tokens=5 cached_content_token_count=None
+ """
+
+ parameter_model = types._CountTokensParameters(
+ model=model,
+ contents=contents,
+ config=config,
+ )
+
+ if self._api_client.vertexai:
+ request_dict = _CountTokensParameters_to_vertex(
+ self._api_client, parameter_model
+ )
+ path = '{model}:countTokens'.format_map(request_dict.get('_url'))
+ else:
+ request_dict = _CountTokensParameters_to_mldev(
+ self._api_client, parameter_model
+ )
+ path = '{model}:countTokens'.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 = _CountTokensResponse_from_vertex(
+ self._api_client, response_dict
+ )
+ else:
+ response_dict = _CountTokensResponse_from_mldev(
+ self._api_client, response_dict
+ )
+
+ return_value = types.CountTokensResponse._from_response(
+ response_dict, parameter_model
+ )
+ self._api_client._verify_response(return_value)
+ return return_value
+
+ async def compute_tokens(
+ self,
+ *,
+ model: str,
+ contents: Union[types.ContentListUnion, types.ContentListUnionDict],
+ config: Optional[types.ComputeTokensConfigOrDict] = None,
+ ) -> types.ComputeTokensResponse:
+ """Return a list of tokens based on the input text.
+
+ This method is not supported by the Gemini Developer API.
+
+ Args:
+ model (str): The model to use.
+ contents (list[shared.Content]): The content to compute tokens for. Only
+ text is supported.
+
+ Usage:
+
+ .. code-block:: python
+
+ response = client.models.compute_tokens(
+ model='gemini-1.5-flash',
+ contents='What is your name?',
+ )
+ print(response)
+ # tokens_info=[TokensInfo(role='user', token_ids=['1841', ...],
+ # tokens=[b'What', b' is', b' your', b' name', b'?'])]
+ """
+
+ parameter_model = types._ComputeTokensParameters(
+ model=model,
+ contents=contents,
+ config=config,
+ )
+
+ if not self._api_client.vertexai:
+ raise ValueError('This method is only supported in the Vertex AI client.')
+ else:
+ request_dict = _ComputeTokensParameters_to_vertex(
+ self._api_client, parameter_model
+ )
+ path = '{model}:computeTokens'.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 = _ComputeTokensResponse_from_vertex(
+ self._api_client, response_dict
+ )
+ else:
+ response_dict = _ComputeTokensResponse_from_mldev(
+ self._api_client, response_dict
+ )
+
+ return_value = types.ComputeTokensResponse._from_response(
+ response_dict, parameter_model
+ )
+ self._api_client._verify_response(return_value)
+ return return_value
+
+ async def generate_content(
+ self,
+ *,
+ model: str,
+ contents: Union[types.ContentListUnion, types.ContentListUnionDict],
+ config: Optional[types.GenerateContentConfigOrDict] = None,
+ ) -> types.GenerateContentResponse:
+ """Makes an API request to generate content using a model.
+
+ Some models support multimodal input and output.
+
+ Usage:
+
+ .. code-block:: python
+
+ from google.genai import types
+ from google import genai
+
+ client = genai.Client(
+ vertexai=True, project='my-project-id', location='us-central1'
+ )
+
+ response = await client.aio.models.generate_content(
+ model='gemini-1.5-flash-002',
+ contents='User input: I like bagels. Answer:',
+ config=types.GenerateContentConfig(
+ system_instruction=
+ [
+ 'You are a helpful language translator.',
+ 'Your mission is to translate text in English to French.'
+ ]
+ ),
+ )
+ print(response.text)
+ # J'aime les bagels.
+ """
+ if _extra_utils.should_disable_afc(config):
+ return await self._generate_content(
+ model=model, contents=contents, config=config
+ )
+ remaining_remote_calls_afc = _extra_utils.get_max_remote_calls_afc(config)
+ logging.info(
+ f'AFC is enabled with max remote calls: {remaining_remote_calls_afc}.'
+ )
+ automatic_function_calling_history = []
+ while remaining_remote_calls_afc > 0:
+ response = await self._generate_content(
+ model=model, contents=contents, config=config
+ )
+ remaining_remote_calls_afc -= 1
+ if remaining_remote_calls_afc == 0:
+ logging.info('Reached max remote calls for automatic function calling.')
+
+ function_map = _extra_utils.get_function_map(config)
+ if not function_map:
+ break
+ if (
+ not response.candidates
+ or not response.candidates[0].content
+ or not response.candidates[0].content.parts
+ ):
+ break
+ func_response_parts = _extra_utils.get_function_response_parts(
+ response, function_map
+ )
+ if not func_response_parts:
+ break
+ contents = t.t_contents(self._api_client, contents)
+ contents.append(response.candidates[0].content)
+ contents.append(
+ types.Content(
+ role='user',
+ parts=func_response_parts,
+ )
+ )
+ automatic_function_calling_history.extend(contents)
+
+ if _extra_utils.should_append_afc_history(config):
+ response.automatic_function_calling_history = (
+ automatic_function_calling_history
+ )
+ return response
+
+ async def list(
+ self,
+ *,
+ config: Optional[types.ListModelsConfigOrDict] = None,
+ ) -> AsyncPager[types.Model]:
+ """Makes an API request to list the available models.
+
+ If `query_base` is set to True in the config, the API will return all
+ available base models. If set to False or not set (default), it will return
+ all tuned models.
+
+ Args:
+ config (ListModelsConfigOrDict): Configuration for retrieving models.
+
+ Usage:
+
+ .. code-block:: python
+
+ response = await client.aio.models.list(config={'page_size': 5})
+ print(response.page)
+ # [Model(name='projects/./locations/./models/123', display_name='my_model'
+
+ response = await client.aio.models.list(
+ config={'page_size': 5, 'query_base': True}
+ )
+ print(response.page)
+ # [Model(name='publishers/google/models/gemini-2.0-flash-exp' ...
+ """
+
+ config = (
+ types._ListModelsParameters(config=config).config
+ or types.ListModelsConfig()
+ )
+ if self._api_client.vertexai:
+ config = config.copy()
+ if config.query_base:
+ http_options = (
+ config.http_options if config.http_options else HttpOptionsDict()
+ )
+ http_options['skip_project_and_location_in_path'] = True
+ config.http_options = http_options
+ else:
+ # Filter for tuning jobs artifacts by labels.
+ filter_value = config.filter
+ config.filter = (
+ filter_value + '&filter=labels.tune-type:*'
+ if filter_value
+ else 'labels.tune-type:*'
+ )
+ if not config.query_base:
+ config.query_base = False
+ return AsyncPager(
+ 'models',
+ self._list,
+ await self._list(config=config),
+ config,
+ )
+
+ async def upscale_image(
+ self,
+ *,
+ model: str,
+ image: types.ImageOrDict,
+ upscale_factor: str,
+ config: Optional[types.UpscaleImageConfigOrDict] = None,
+ ) -> types.UpscaleImageResponse:
+ """Makes an API request to upscale a provided image.
+
+ Args:
+ model (str): The model to use.
+ image (Image): The input image for upscaling.
+ upscale_factor (str): The factor to upscale the image (x2 or x4).
+ config (UpscaleImageConfig): Configuration for upscaling.
+
+ Usage:
+
+ .. code-block:: python
+
+ from google.genai.types import Image
+
+ IMAGE_FILE_PATH="my-image.png"
+ response = await client.aio.models.upscale_image(
+ model='imagen-3.0-generate-001',
+ image=types.Image.from_file(IMAGE_FILE_PATH),
+ upscale_factor='x2',
+ )
+ response.generated_images[0].image.show()
+ # Opens my-image.png which is upscaled by a factor of 2.
+ """
+
+ # Validate config.
+ types.UpscaleImageParameters(
+ model=model,
+ image=image,
+ upscale_factor=upscale_factor,
+ config=config,
+ )
+
+ # Convert to API config.
+ config = config or {}
+ config_dct = config if isinstance(config, dict) else config.dict()
+ api_config = types._UpscaleImageAPIConfigDict(**config_dct) # pylint: disable=protected-access
+
+ # Provide default values through API config.
+ api_config['mode'] = 'upscale'
+ api_config['number_of_images'] = 1
+
+ return await self._upscale_image(
+ model=model,
+ image=image,
+ upscale_factor=upscale_factor,
+ config=api_config,
+ )