diff options
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.py | 5567 |
1 files changed, 5567 insertions, 0 deletions
diff --git a/.venv/lib/python3.12/site-packages/google/genai/models.py b/.venv/lib/python3.12/site-packages/google/genai/models.py new file mode 100644 index 00000000..b23428b6 --- /dev/null +++ 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, + ) |