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aspectRatioguidance_scale guidanceScaleseedsafety_filter_level safetySettingperson_generationpersonGenerationinclude_safety_attributesincludeSafetyAttributesinclude_rai_reasonincludeRaiReasonlanguageoutput_mime_typer outputOptionsr.output_compression_qualityrrcompressionQuality add_watermark addWatermarklabels edit_modeeditMode base_steps)r editConfig baseStepsrRr2r r r!_EditImageConfig_to_vertex               rc sit|dgdurtddgt|t|dgt|dgdur.tddgt|dgt|dgdurItddgfddt|dgDt|d gdurZtt|d gS) NrTrUprompt instances[0]reference_imagesreferenceImagescrgr )_ReferenceImageAPI_to_vertexrGrir r!rJbrjz2_EditImageParameters_to_vertex..r)r0r1rWrXrrr rir!_EditImageParameters_to_vertexNs(   rcbit|dgdurtdgt|dgt|dgdur/tdgfddt|dgDS)Nr\r] predictionsgenerated_imagescrgr _GeneratedImage_from_vertexrGrir r!rJ|rjz2_EditImageResponse_from_vertex..rRrlr rir!_EditImageResponse_from_vertexn   rcCsi}t|dgdurt|ddgt|dgt|dgdur*t|ddgt|dgt|dgdur>t|ddgt|dgt|dgdurJtdt|d gdurVtd |S) N task_type requests[]taskTypetitleoutput_dimensionalityoutputDimensionalityr-z3mime_type parameter is not supported in Gemini API. auto_truncatez7auto_truncate parameter is not supported in Gemini API.r/r2r r r!_EmbedContentConfig_to_mldevs(  rcCsi}t|dgdurt|ddgt|dgt|dgdur*t|ddgt|dgt|dgdur>t|ddgt|dgt|dgdurRt|ddgt|dgt|d gdurft|dd gt|d g|S) Nr instances[]rrrrr-r.r autoTruncaterRr2r r r!_EmbedContentConfig_to_vertexs8    rc Csi}t|dgdurt|ddgt|t|dgt|dgdur7t|ddgddt|t|dgDt|dgdurHtt|dg|t|ddgt|t|dg|S) NrTrUrVrr6cSrFr r rGr r r!rJz4_EmbedContentParameters_to_mldev..r)r0r1rWrXt_contents_for_embedrrZr r r! _EmbedContentParameters_to_mldevs2  rc Csi}t|dgdurt|ddgt|t|dgt|dgdur7t|ddgddt|t|dgDt|dgdurHtt|dg||S) NrTrUrVrr6cSrFr r rGr r r!rJrz5_EmbedContentParameters_to_vertex..r)r0r1rWrXrrrZr r r!!_EmbedContentParameters_to_vertexs(  rcCsi}t|dgdurt|dgt|dgt|dgdur-t|dgddt|dgDt|dgdur@t|dgt|dg|S)Nr\r] embeddingscSrFr r rGr r r!rJrKz4_EmbedContentResponse_from_mldev..metadatarRr2r r r! 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API.rz9negative_prompt parameter is not supported in Gemini API.rrrrrrrr.seed parameter is not supported in Gemini API.rrrrrrrrrrrrrrz7add_watermark parameter is not supported in Gemini API.rr  image_sizesampleImageSizeenhance_promptz8enhance_prompt parameter is not supported in Gemini API.)r0rr1r%r"r2r r r!_GenerateImagesConfig_to_mldevIs             r9cCsi}t|dgdurt|ddgt|dgt|dgdur*t|ddgt|dgt|dgdur>t|ddgt|dgt|dgdurRt|dd gt|dgt|d gdurft|dd gt|d gt|d gdurzt|dd gt|d gt|d gdurt|ddgt|d gt|dgdurt|ddgt|dgt|dgdurt|ddgt|dgt|dgdurt|ddgt|dgt|dgdurt|ddgt|dgt|dgdurt|gdt|dgt|dgdurt|gdt|dgt|dgdurt|ddgt|dgt|dgdur0t|dgt|dgt|dgdurEt|ddgt|dgt|dgdurZt|dd gt|dg|S)!Nrrrrrrrrrrrrrrrrrrrrrrrrrrrrr6r7r8 enhancePromptrRr2r r r!_GenerateImagesConfig_to_vertexrr;c Ci}t|dgdurt|ddgt|t|dgt|dgdur.t|ddgt|dgt|dgdur?tt|dg||SNrTrUrrr)r0r1rWrXr9rZr r r!"_GenerateImagesParameters_to_mldev1r>c Cr<r=)r0r1rWrXr;rZr r r!#_GenerateImagesParameters_to_vertexGr?r@cit|dgdurtdgt|dgt|dgdur/tdgfddt|dgDt|dgdurEtdgtt|dgS) Nr\r]rrcrgr )_GeneratedImage_from_mldevrGrir r!rJkrjz6_GenerateImagesResponse_from_mldev..positivePromptSafetyAttributes!positive_prompt_safety_attributes)r0r1_SafetyAttributes_from_mldevrlr rir!"_GenerateImagesResponse_from_mldev],    rFcrA) Nr\r]rrcrgr rrGrir r!rJrjz7_GenerateImagesResponse_from_vertex..rCrD)r0r1_SafetyAttributes_from_vertexrlr rir!#_GenerateImagesResponse_from_vertex}rGrIcs0it|dgdurt|ddgt|dgt|dgdur"tdt|dgdur.tdt|dgdurBt|dd gt|dgt|d gdurNtd t|d gdurbt|dd gt|d gt|dgdurvt|ddgt|dgt|dgdurt|ddgt|dgt|dgdurtdt|dgdurt|ddgt|dgt|dgdurt|ddgt|dgt|dgdurtdt|dgdurt|ddgtt|dgt|dgdurt|ddgfddt|dgDt|d gdur td!t|d"gdurtd#S)$Nnumber_of_videosrrrr4fpsz-fps parameter is not supported in Gemini API.duration_secondsdurationSecondsrr5rr resolutionrr pubsub_topicz6pubsub_topic parameter is not supported in Gemini API.rrr8r:generate_audioz8generate_audio parameter is not supported in Gemini API. last_framer lastFramerrcrgr )'_VideoGenerationReferenceImage_to_mldevrGrir r!rJrjz2_GenerateVideosConfig_to_mldev..maskz.mask parameter is not supported in Gemini API.compression_qualityz=compression_quality parameter is not supported in Gemini API.)r0r1r_Image_to_mldevrlr rir!_GenerateVideosConfig_to_mldevs          rWcsit|dgdurt|ddgt|dgt|dgdur*t|ddgt|dgt|dgdur>t|ddgt|dgt|dgdurRt|ddgt|dgt|d gdurft|dd gt|d gt|d gdurzt|dd gt|d gt|d gdurt|dd gt|d gt|d gdurt|ddgt|d gt|dgdurt|ddgt|dgt|dgdurt|ddgt|dgt|dgdurt|ddgt|dgt|dgdurt|ddgt|dgt|dgdur t|ddgtt|dgt|dgdur&t|ddgfddt|dgDt|dgdur>t|ddgtt|dgt|dgdurSt|dd gt|dgS)!NrJrrrrrKrLrMrrrrNrrrO pubsubTopicrrr8r:rP generateAudiorQrrRrrcrgr )(_VideoGenerationReferenceImage_to_vertexrGrir r!rJ\rjz3_GenerateVideosConfig_to_vertex..rTrUr)r0r1_Image_to_vertex_VideoGenerationMask_to_vertexrlr rir!_GenerateVideosConfig_to_vertexs              r]cCsi}t|dgdurt|dgt|dgt|dgdur(t|dgt|dgt|dgdur;t|dgt|dgt|dgdurNt|dgt|dgt|ddgdurft|dgtt|ddg|t|ddgdur~t|dgtt|ddg||S)NrrdoneerrorrgenerateVideoResponseresult)r0r1"_GenerateVideosResponse_from_mldevr2r r r!#_GenerateVideosOperation_from_mldevss4rccCsi}t|dgdurt|dgt|dgt|dgdur(t|dgt|dgt|dgdur;t|dgt|dgt|dgdurNt|dgt|dgt|dgdurdt|dgtt|dg|t|dgdurzt|dgtt|dg||S)Nrrr^r_rra)r0r1#_GenerateVideosResponse_from_vertexr2r r r!$_GenerateVideosOperation_from_vertexs4  rec Ci}t|dgdurt|ddgt|t|dgt|dgdur.t|ddgt|dgt|dgdurEt|ddgtt|dg|t|dgdur\t|ddgtt|dg|t|dgdurmtt|dg|t|dgdur~tt|dg||S NrTrUrrimagevideosourcer)r0r1rWrXrV_Video_to_mldev_GenerateVideosSource_to_mldevrWrZr r r!"_GenerateVideosParameters_to_mldev4rmc Crfrg)r0r1rWrXr[_Video_to_vertex_GenerateVideosSource_to_vertexr]rZr r r!#_GenerateVideosParameters_to_vertexrnrqcit|dgdurtdgfddt|dgDt|dgdur/tdgt|dgt|dgdurBtdgt|dgS) NgeneratedSamplesgenerated_videoscrgr )_GeneratedVideo_from_mldevrGrir r!rJrjz6_GenerateVideosResponse_from_mldev..raiMediaFilteredCountrai_media_filtered_countraiMediaFilteredReasonsrai_media_filtered_reasonsrRrlr rir!rb ,     rbcrr) Nvideosrtcrgr )_GeneratedVideo_from_vertexrGrir r!rJ6rjz7_GenerateVideosResponse_from_vertex..rvrwrxryrRrlr rir!rd-rzrdcCi}t|dgdurt|ddgt|dgt|dgdur-t|ddgtt|dg|t|dgdurDt|ddgtt|dg||SNrrrhri)r0r1rVrkr2r r r!rlM$rlcCr}r~)r0r1r[ror2r r r!rphrrpcCsdi}t|dgdurt|dgtt|dg|t|dgdur0t|dgddt|dgD|S)N_selfrTrcSrFr r rGr r r!rJrKz3_GeneratedImageMask_from_vertex..)r0r1_Image_from_vertexr2r r r!_GeneratedImageMask_from_vertexsrcCsi}t|dgdurt|dgtt|dg|t|dgdur+t|dgt|dgt|dgdurAt|dgtt|dg||S)NrrhraiFilteredReasonrai_filtered_reasonsafety_attributes)r0r1_Image_from_mldevrEr2r r r!rBs( rBcCsi}t|dgdurt|dgtt|dg|t|dgdur+t|dgt|dgt|dgdurAt|dgtt|dg|t|dgdurTt|dgt|dg|S)Nrrhrrrrenhanced_prompt)r0r1rrHr2r r r!rs, rcC4i}t|dgdurt|dgtt|dg||S)Nri)r0r1_Video_from_mldevr2r r r!rurucC4i}t|dgdurt|dgtt|dg||S)Nrri)r0r1_Video_from_vertexr2r r r!r|rr|cCs~i}t|dgdurt|dgt|dgt|dgdur(t|dgt|dgt|dgdur;t|dgt|dgt|dgdurNt|dgt|dgt|d gdurat|d gt|d gt|d gdurtt|d gt|d gt|d gdurt|d gt|d gt|dgdurt|dgt|dgt|dgdurt|dgt|dgt|dgdurt|dgt|dgt|dgdurt|dgt|dgt|dgdurt|dgt|dgt|dgdurt|dgt|dgt|dgdur t|dgt|dgt|dgdur!t|dgt|dgt|dgdur5t|dgt|dgt|dgdurLt|d gtt|dg|t|d!gdur`t|d"gt|d!gt|d#gdurtt|d#gt|d#gt|d$gdurt|d%gt|d$gt|d&gdurt|d'gt|d&gt|d(gdurt|d)gt|d(gt|d*gdurtd+|S),Nrr!rrrr"rrenable_affective_dialogenableAffectiveDialogrrrrrrrrrrrrrrrrrrr rrrrrrrrrrrrenable_enhanced_civic_answerszFenable_enhanced_civic_answers parameter is not supported in Vertex AI.)r0r1r#rr2r r r!r|s        r|c CrrrrZr r r!_GetModelParameters_to_mldevi rrc CrrrrZr r r!_GetModelParameters_to_vertexy rrcCsFi}t|dgdurtdt|dgdur!t|dgt|dg|S)N auth_configz5auth_config parameter is not supported in Gemini API. enable_widget enableWidgetrr2r r r!_GoogleMaps_to_mldev s rcCs^i}t|dgdurtdt|dgdurtdt|dgdur-t|dgt|dg|S)Nexclude_domainsz9exclude_domains parameter is not supported in Gemini API.blocking_confidencez=blocking_confidence parameter is not supported in Gemini API.time_range_filtertimeRangeFilterrr2r r r!_GoogleSearch_to_mldev srcCsZi}t|dgdurt|dgtt|dgt|dgdur+t|dgt|dg|S)NbytesBase64Encoded image_bytesr.r-r0r1base_tt_bytesr2r r r!r srcCi}t|dgdurt|dgt|dgt|dgdur+t|dgtt|dgt|dgdur>t|dgt|dg|S)NgcsUrigcs_urirrr.r-rr2r r r!r rcCsri}t|dgdurtdt|dgdur$t|dgtt|dgt|dgdur7t|dgt|dg|S)Nrz1gcs_uri parameter is not supported in Gemini API.rrr-r.)r0rr1rrr2r r r!rV srVcCr)Nrrrrr-r.rr2r r r!r[ rr[c Ci}t|dgdurt|ddgt|dgt|dgdur*t|ddgt|dgt|dgdur>t|ddgt|dgt|dgdurVt|dd gt|t|dg|S N page_size_querypageSize page_token pageTokenfilter query_baserU models_urlr0r1rW t_models_urlrZr r r!_ListModelsConfig_to_mldev ( rc CrrrrZr r r!_ListModelsConfig_to_vertex rrcC,i}t|dgdurt|t|dg||SNr)r0rrZr r r!_ListModelsParameters_to_mldev@ rcCrr)r0rrZr r r!_ListModelsParameters_to_vertexN rrc it|dgdurtdgt|dgt|dgdur(tdgt|dgt|dgdurEtdgfddtt|dgDS) Nr\r] nextPageTokennext_page_tokenrmodelscrgr )_Model_from_mldevrGrir r!rJm rjz2_ListModelsResponse_from_mldev..r0r1rWt_extract_modelsrlr rir!_ListModelsResponse_from_mldev\   rc r) Nr\r]rrrrcrgr )_Model_from_vertexrGrir r!rJ rjz3_ListModelsResponse_from_vertex..rrlr rir!_ListModelsResponse_from_vertexv rrcCr)N mask_modemaskModesegmentation_classes maskClasses mask_dilationdilationrRr2r r r!_MaskReferenceConfig_to_vertex srcCsi}t|dgdurt|dgt|dgt|dgdur(t|dgt|dgt|dgdur;t|dgt|dgt|dgdurNt|dgt|dgt|dgdurat|dgt|dgt|dgdurtt|d gt|dgt|d gdurt|d gt|d gt|d gdurt|d gt|d gt|dgdurt|dgt|dgt|dgdurt|dgt|dgt|dgdurt|dgt|dgt|dgdurt|dgt|dgt|dgdurt|dgt|dg|S)Nr displayNamer+rversionrtuned_model_infoinputTokenLimitinput_token_limitoutputTokenLimitoutput_token_limitsupportedGenerationMethodssupported_actionsrmaxTemperaturemax_temperaturerrrrthinkingrRr2r r r!r sL  rcs|it|dgdurtdgt|dgt|dgdur(tdgt|dgt|dgdur;tdgt|dgt|dgdurNtdgt|dgt|dgdurhtdgfd d t|dgDt|d gdur{td gt|d gt|d gdurtd gtt|d gt|dgdurtdgt|dgt|dgdurtdgdd t|dgDS)Nrrr+r versionIdrdeployedModels endpointscrgr )rrGrir r!rJ rjz&_Model_from_vertex..rrrdefaultCheckpointIddefault_checkpoint_id checkpointscSrFr r rGr r r!rJ rK)r0r1_TunedModelInfo_from_vertexrlr rir!r sL    rcCsi}t|dgdurt|dgt|dgt|dgdur(t|dgt|dgt|dgdur;t|dgt|dgt|dgdurQt|dgtt|dg|t|d gdurdt|d gt|d gt|d gdurzt|d gtt|d g|t|d gdurt|d gt|d gt|dgdurt|dgt|dgt|dgdurt|dgt|dgt|dgdurt|dgt|dg|S)N function_call functionCallcode_execution_resultcodeExecutionResultexecutable_codeexecutableCode file_datafileDatafunction_responsefunctionResponse inline_data inlineDatatextthoughtthought_signaturethoughtSignaturevideo_metadata videoMetadata)r0r1rr4r2r r r!rh sT   rhcCr)N product_imagerhr0r1r[r2r r r!_ProductImage_to_vertexL rrcCsi}t|dgdurt|ddgt|dgt|dgdur*t|ddgt|dgt|dgdur>t|ddgt|dgt|dgdurRt|ddgt|dgt|d gdurft|dd gt|d gt|d gdurzt|dd gt|d gt|d gdurt|ddgt|d gt|dgdurt|gdt|dgt|dgdurt|gdt|dgt|dgdurt|ddgt|dgt|dgdurt|dgt|dg|S)Nrrrrrrrrrrrrrrrrrrr8r:rrRr2r r r!_RecontextImageConfig_to_vertex[ sx         rc C|i}t|dgdurt|ddgt|t|dgt|dgdur+tt|dg|t|dgdur.rRrlr rir!#_RecontextImageResponse_from_vertex    rcsit|dgdurt|ddgt|dgt|dgdur-t|gdtt|dgt|dgdurHt|ddgfddt|dgDS) Nrr person_image)r personImagerhproduct_images productImagescrgr )rrGrir r!rJ rjz3_RecontextImageSource_to_vertex..rrlr rir!r s(   rcCs$i}t|dgdurt|dgtt|dg|t|dgdur+t|dgt|dgt|dgdur>t|dgt|dgt|dgdurTt|dgtt|dg|t|d gdurjt|d gtt|d g|t|d gdur}t|d gt|d gt|d gdurt|dgt|d g|S)Nreference_imagereferenceImage reference_id referenceIdreference_type referenceTypemask_image_configmaskImageConfigcontrol_image_configcontrolImageConfigstyle_image_configstyleImageConfigsubject_image_configsubjectImageConfig)r0r1r[rrsr2r r r!r sP    rcCi}t|ddgdurt|dgt|ddgt|ddgdur,t|dgt|ddgt|dgdur?t|dgt|dg|SNsafetyAttributes categoriesscores contentType content_typerRr2r r r!rE$  rEcCr rrRr2r r r!rH; rrHcCsli}t|dgdurt|dgt|dgt|dgdur!tdt|dgdur4t|dgt|dg|S)Ncategorymethodz0method parameter is not supported in Gemini API. thresholdr/r2r r r!rR r5rcCr)Nrhrr2r r r!_ScribbleImage_to_vertexc rrcCsi}t|dgdurt|ddgt|dgt|dgdur*t|ddgt|dgt|dgdur>t|ddgt|dgt|dgdurRt|ddgt|dgt|d gdurft|dd gt|d gt|d gduryt|d gt|d g|S) Nmodermax_predictionsmaxPredictionsconfidence_thresholdconfidenceThresholdr maskDilationbinary_color_thresholdbinaryColorThresholdrrRr2r r r!_SegmentImageConfig_to_vertexr s<    r!c Crr)r0r1rWrX_SegmentImageSource_to_vertexr!rZr r r!!_SegmentImageParameters_to_vertex rr#cr)Nrgenerated_maskscrgr )rrGrir r!rJ rjz5_SegmentImageResponse_from_vertex..rRrlr rir!!_SegmentImageResponse_from_vertex rr%cCsi}t|dgdurt|ddgt|dgt|dgdur-t|ddgtt|dg|t|dgdurDt|ddgtt|dg||S)Nrrrhscribble_imagescribble)r0r1r[rr2r r r!r" s( r"cCsli}t|dgdurt|dgt|dgt|dgdur(t|dgt|dgt|dgdur4td|S)N language_code languageCode voice_config voiceConfigmulti_speaker_voice_configzCmulti_speaker_voice_config parameter is not supported in Vertex AI.r/r2r r r!r# sr#cCs~i}t|dgdurt|dgddt|dgDt|dgdur&tdt|dgdur9t|dgt|dgt|d gdurLt|d gt|d gt|d gdur_t|d gt|d gt|d gdurrt|dgt|d gt|dgdur~tdt|dgdurt|dgtt|dg|t|dgdurt|dgtt|dg|t|dgdurt|dgt|dg|S)Nfunction_declarationsfunctionDeclarationscSrFr r rGr r r!rJ rKz"_Tool_to_mldev.. retrievalz3retrieval parameter is not supported in Gemini API.google_search_retrievalgoogleSearchRetrieval computer_use computerUse file_search fileSearchcode_execution codeExecutionenterprise_web_searchz?enterprise_web_search parameter is not supported in Gemini API. google_maps googleMaps google_search googleSearch url_context urlContext)r0r1rrrr2r r r!r sP rcsit|dgdurtdgfddt|dgDt|dgdur/tdgt|dgt|dgdurBtdgt|dgt|dgdurUtd gt|dgt|d gduratd t|d gdurttd gt|d gt|dgdurtdgt|dgt|dgdurtdgt|dgt|dgdurtdgt|dgt|dgdurtdgt|dgS)Nr-r.crgr )rrGrir r!rJ6 rjz#_Tool_to_vertex..r/r0r1r2r3r4z4file_search parameter is not supported in Vertex AI.r6r7r8enterpriseWebSearchr9r:r;r<r=r>r/rlr rir!ry- sH     rycCs~i}t|ddgdurt|dgt|ddgt|dgdur*t|dgt|dgt|dgdur=t|dgt|dg|S)Nrz&google-vertex-llm-tuning-base-model-id base_modelr1r2 updateTime update_timerRr2r r r!rb s  rcCzi}t|dgdurt|dgt|dgt|dgdur(t|dgt|dgt|dgdur;t|dgt|dg|SNr+rrrrrRr2r r r!_UpdateModelConfig_to_mldevz  rEcCrCrDrRr2r r r!_UpdateModelConfig_to_vertex rFrGc CsZi}t|dgdurt|ddgt|t|dgt|dgdur+tt|dg||S)NrTrUrr)r0r1rWrXrErZr r r!_UpdateModelParameters_to_mldev rHc CsZi}t|dgdurt|ddgt|t|dgt|dgdur+tt|dg||S)NrTrUr)r0r1rWrXrGrZr r r! _UpdateModelParameters_to_vertex rIrJcCsi}t|dgdurt|ddgt|dgt|dgdur*t|ddgt|dgt|dgdur>t|ddgt|dgt|dgdurRt|dd gt|dgt|d gdurft|gd t|d gt|d gdurzt|gd t|d gt|dgdurt|gdt|dgt|dgdurt|gdt|dgt|dgdurt|dgt|dgt|dgdurt|ddgt|dgt|dgdurt|ddgt|dg|S)Nrrrrrrrrrrrrrenhance_input_image)r upscaleConfigenhanceInputImageimage_preservation_factor)rrLimagePreservationFactorrrrrrRr2r r r! _UpscaleImageAPIConfig_to_vertex sx         rPc Csi}t|dgdurt|ddgt|t|dgt|dgdur1t|ddgtt|dg|t|dgdurEt|gdt|dgt|dgdurVtt|dg||S)NrTrUrhrupscale_factor)rrL upscaleFactorr)r0r1rWrXr[rPrZr r r!$_UpscaleImageAPIParameters_to_vertexs, rScr)Nr\r]rrcrgr rrGrir r!rJIrjz5_UpscaleImageResponse_from_vertex..rRrlr rir!!_UpscaleImageResponse_from_vertex;rrTcCsZi}t|dgdurt|dgtt|dg|t|dgdur+t|dgt|dg|S)Nrhrrrrr2r r r!r\Rr\cCsji}t|dgdurt|dgtt|dg|t|dgdur3tt|dgt|dgt|dg|SNrhrr)r0r1rVr'r2r r r!rSds rScCsZi}t|dgdurt|dgtt|dg|t|dgdur+t|dgt|dg|SrVrr2r r r!rZyrUrZcCi}t|dgdurt|dgt|dgt|dgdur+t|dgtt|dgt|dgdur>t|dgt|dg|S)Nuri encodedVideo video_bytesencodingr-rr2r r r!rrrcCr)NrrXrrZr.r-rr2r r r!rrrcCrW)NrXrZrYr-r[rr2r r r!rkrrkcCr)NrXrrZrr-r.rr2r r r!rorroc@s$eZdZdddedeejejfdeej dej fddZ dddedeejejfdeej de ej fd d Z dddedeejejfdeejdejfd d Zddded edeejdejfddZddded edeejdeejdejf ddZdddedejdedeejdejf ddZdddedejdeejdejfddZ dddedej!deej"dej#fddZ$dddedeej%dej&fddZ'dddeej(dej)fddZ*dddedeej+dej&fd d!Z,dddedeej-dej.fd"d#Z/dddedeejejfdeej0dej1fd$d%Z2dddedeejejfdeej3dej4fd&d'Z5dddddd(ded eedeejd)eej6deej7deej8dej9fd*d+Z:dddedejdeej dej fd,d-Z;dddedejdeej de ej fd.d/Zdddedejdedeej?dejf d4d5Z@dddddd(ded eedeejd)eej6deej7deej8dej9fd6d7ZAdddeej(deBej&fd8d9ZdS):ModelsNrrTrVrrc Cs|tj|||d}|jjr"t|j|}|d}|rd|}nd}nt|j|}|d}|r5d|}nd}|d}|rG|dt|}| ddd} |j dur^|j j dur^|j j } t |}t |}|jd||| } |durt|ddrtj| d } |j| | S| jsint| j} |jjrt| } |jjst| } tjj| |d } tj| jd | _|j| | S NrTrVrrUz{model}:generateContentr?rpostshould_return_http_response)r]rkwargsheaders)r_GenerateContentParameters _api_clientvertexair&get format_mapr%rpopr http_optionsr convert_to_dictencode_unserializable_typesrequestgetattrGenerateContentResponse_verify_responsebodyjsonloadsr3r0_from_response model_dump HttpResponserfr] selfrTrVrparameter_model request_dictrequest_url_dictpath query_paramsrmr return_value response_dictr r r!_generate_contentsh              zModels._generate_contentc csvtj|||d}|jjr#t|j|}|d}|r d|}nd}nt|j|}|d}|r6d|}nd}|d}|rH|dt|}| ddd} |j dur_|j j dur_|j j } t |}t |}|durwt|ddrwtd|jd ||| D]8} | jsint| j} |jjrt| } |jjst| } tjj| |d } tj| jd | _|j| | VqdS) Nr_rU%{model}:streamGenerateContent?alt=sserr`rrbFAccessing the raw HTTP response is not supported in streaming methods.rarcre)rrgrhrir&rjrkr%rrlrrmr rnrorqrrequest_streamedrtrurvr3r0rrrwrxryrfr]rs r{rTrVrr|r}r~rrrmrrrr r r!_generate_content_stream.st          zModels._generate_content_streamc CsLtj|||d}|jjr"t|j|}|d}|rd|}nd}nt|j|}|d}|r5d|}nd}|d}|rG|dt|}| ddd} |j dur^|j j dur^|j j } t |}t |}|jd ||| } | jsvint| j} |jjrt| } |jjst| } tjj| |d } tj| jd | _|j| | S) aACalculates 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 }, ) r_rU{model}:predict{model}:batchEmbedContentsrr`rNrarcre)r_EmbedContentParametersrhrirrjrkrrrlrrmr rnrorprtrurvrrEmbedContentResponserwrxryrfr]rsrr r r! embed_contents\           zModels.embed_contentrc CLtj|||d}|jjr"t|j|}|d}|rd|}nd}nt|j|}|d}|r5d|}nd}|d}|rG|dt|}| ddd} |j dur^|j j dur^|j j } t |}t |}|jd||| } | jsvint| j} |jjrt| } |jjst| } tjj| |d } tj| jd | _|j| | S) z%Private method for generating images.rTrrrUrrr`rNrarcre)r_GenerateImagesParametersrhrir@rjrkr>rrlrrmr rnrorprtrurvrIrFGenerateImagesResponserwrxryrfr]rs r{rTrrr|r}r~rrrmrrrr r r!_generate_imagess\            zModels._generate_imagesrcCtj||||d}|jjstdt|j|}|d}|r$d|}nd}|d} | r6|dt| }| ddd} |j durM|j j durM|j j } t |}t |}|jd ||| } | jseint| j} |jjrst| } tjj| |d } tj| jd | _|j| | S) z$Private method for editing an image.rTrrr6This method is only supported in the Vertex AI client.rUrrr`rNrarcre)r_EditImageParametersrhrirrrjrkrrlrrmr rnrorprtrurvrEditImageResponserwrxryrfr]rsr{rTrrrr|r}r~rrrmrrrr r r! _edit_image%N          zModels._edit_imagerhrQcCr) z&Private method for upscaling an image.rTrhrQrrrUrrr`rNrarcre)r_UpscaleImageAPIParametersrhrirrSrjrkrrlrrmr rnrorprtrurvrTUpscaleImageResponserwrxryrfr]rsr{rTrhrQrr|r}r~rrrmrrrr r r!_upscale_imageerzModels._upscale_imagerjc Ctj|||d}|jjstdt|j|}|d}|r#d|}nd}|d}|r5|dt|}| ddd} |j durL|j j durL|j j } t |}t |}|jd ||| } | jsdint| j} |jjrrt| } tjj| |d } |j| | S aRecontextualizes an image. There are two types of recontextualization currently supported: 1) Imagen Product Recontext - Generate images of products in new scenes and contexts. 2) Virtual Try-On: Generate images of persons modeling fashion products. Args: model (str): The model to use. source (RecontextImageSource): An object containing the source inputs (prompt, person_image, product_images) for image recontext. prompt is optional for product recontext and disallowed for virtual try-on. person_image is required for virtual try-on, disallowed for product recontext. product_images is required for both product recontext and virtual try-on. Only one product image is supported for virtual try-on, and up to 3 product images (different angles of the same product) are supported for product recontext. config (RecontextImageConfig): Configuration for recontextualization. Usage: ``` product_recontext_response = client.models.recontext_image( model="imagen-product-recontext-preview-06-30", source=types.RecontextImageSource( prompt="In a modern kitchen setting.", product_images=[types.ProductImage.from_file(IMAGE_FILE_PATH)], ), config=types.RecontextImageConfig( number_of_images=1, ), ) image = product_recontext_response.generated_images[0].image virtual_try_on_response = client.models.recontext_image( model="virtual-try-on-preview-08-04", source=types.RecontextImageSource( person_image=types.Image.from_file(IMAGE1_FILE_PATH), product_images=[types.ProductImage.from_file(IMAGE2_FILE_PATH)], ), config=types.RecontextImageConfig( number_of_images=1, ), ) image = virtual_try_on_response.generated_images[0].image ``` rTrjrrrUrrr`rNrarc)r_RecontextImageParametersrhrirrrjrkrrlrrmr rnrorprtrurvrRecontextImageResponserwrxrs r{rTrjrr|r}r~rrrmrrrr r r!recontext_imagesF7         zModels.recontext_imagec Cr) a"Segments an image, creating a mask of a specified area. Args: model (str): The model to use. source (SegmentImageSource): An object containing the source inputs (prompt, image, scribble_image) for image segmentation. The prompt is required for prompt mode and semantic mode, disallowed for other modes. scribble_image is required for the interactive mode, disallowed for other modes. config (SegmentImageConfig): Configuration for segmentation. Usage: ``` response = client.models.segment_image( model="image-segmentation-001", source=types.SegmentImageSource( image=types.Image.from_file(IMAGE_FILE_PATH), ), ) mask_image = response.generated_masks[0].mask ``` rrrUrrr`rNrarc)r_SegmentImageParametersrhrirr#rjrkrrlrrmr rnrorprtrurvr%SegmentImageResponserwrxrsrr r r! segment_imagesF          zModels.segment_imagec Cs:tj||d}|jjr!t|j|}|d}|rd|}nd}nt|j|}|d}|r4d|}nd}|d}|rF|dt|}| ddd}|j dur]|j j dur]|j j }t |}t |}|jd|||} | jsuint| j} |jjrt| } |jjst| } tjj| |d} |j| | S NrTrrU{name}rr`rrjrc)r_GetModelParametersrhrirrjrkrrrlrrmr rnrorprtrurvrrModelrwrxrs r{rTrr|r}r~rrrmrrrr r r!rjbsP           z Models.getc Cstj|d}|jjr t|j|}|d}|rd|}nd}nt|j|}|d}|r3d|}nd}|d}|rY|drY|d}|d|}|rX|dt |7}n |rd|dt |}|d dd}|j dur{|j j dur{|j j }t |}t |}|jd |||} | jsint| j} |jjrt| } |jjst| } tjj| |d } tj| jd | _|j| | S Nr]rUz {models_url}rrz?filter=&r`rrjrcre)r_ListModelsParametersrhrirrjrkrrlrrrmr rnrorprtrurvrrListModelsResponserwrxryrfr]rs r{rr|r}r~rrquery_param_filterrmrrrr r r!_lists`            z Models._listc Cs:tj||d}|jjr!t|j|}|d}|rd|}nd}nt|j|}|d}|r4d|}nd}|d}|rF|dt|}| ddd}|j dur]|j j dur]|j j }t |}t |}|jd|||} | jsuint| j} |jjrt| } |jjst| } tjj| |d } |j| | S NrrUz{model}rrr`rpatchrc)r_UpdateModelParametersrhrirJrjrkrHrrlrrmr rnrorprtrurvrrrrwrxrsrr r r!updatesT           z Models.updatec CsJtj||d}|jjr!t|j|}|d}|rd|}nd}nt|j|}|d}|r4d|}nd}|d}|rF|dt|}| ddd}|j dur]|j j dur]|j j }t |}t |}|jd|||} | jsuint| j} |jjrt| } |jjst| } tjj| |d} tj| jd | _|j| | S NrrUrrr`rdeletercre)r_DeleteModelParametersrhrirrjrkrrrlrrmr rnrorprtrurvrrDeleteModelResponserwrxryrfr]rsrr r r!r$sZ           z Models.deletec Cr) a8Counts the number of tokens in the given content. Multimodal input is supported for Gemini models. Args: model (str): The model to use for counting tokens. contents (list[types.Content]): The content to count tokens for. config (CountTokensConfig): The configuration for counting tokens. Usage: .. code-block:: python response = client.models.count_tokens( model='gemini-2.0-flash', contents='What is your name?', ) print(response) # total_tokens=5 cached_content_token_count=None r_rU{model}:countTokensrr`rNrarcre)r_CountTokensParametersrhrirrjrkrrrlrrmr rnrorprtrurvrrCountTokensResponserwrxryrfr]rsrr r r! count_tokenshs\           zModels.count_tokensc Cstj|||d}|jjstdt|j|}|d}|r#d|}nd}|d}|r5|dt|}| ddd} |j durL|j j durL|j j } t |}t |}|jd ||| } | jsdint| j} |jjrrt| } tjj| |d } tj| jd | _|j| | S) azGiven a list of contents, returns a corresponding TokensInfo containing the list of tokens and list of token ids. 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. Usage: .. code-block:: python response = client.models.compute_tokens( model='gemini-2.0-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'?'])] r_rrU{model}:computeTokensrr`rNrarcre)r_ComputeTokensParametersrhrirr[rjrkrrlrrmr rnrorprtrurvr`ComputeTokensResponserwrxryrfr]rsrr r r!compute_tokenssL         zModels.compute_tokensrrhrirjrricCsBtj||||||d}|jjr%t|j|}|d} | r"d| } nd} nt|j|}|d} | r8d| } nd} |d} | rJ| dt| } | ddd} |j dura|j j dura|j j } t |}t |}|jd| || } | jsyint| j}|jjrt|}|jjst|}tjj||d }|j||S) z%Private method for generating videos.rTrrhrirjrrU{model}:predictLongRunningrr`rNrarc)r_GenerateVideosParametersrhrirqrjrkrmrrlrrmr rnrorprtrurvrercGenerateVideosOperationrwrxrsr{rTrrhrirjrr|r}r~rrrmrrrr r r!_generate_videoss\             zModels._generate_videoscCst|}t|}|r|jrt|jrtdt|r'|j |||dS|rgd}t |t j r:|jr9t |j}nt |trK|dg}|rKt |}t ||kr_dtt|}td||j |||dSt|} td| dg} t } d} | dkr| d 7} |j |||d} t|} | sni| snf| jr| jdjr| jdjjsnUt| | }|snLtd | d | d 8} | dkrtd | jdj}t jd |d}t|}| s| |t |t!r|"||"|| "|| "|| dkst#|r | dur | | _$| S)aMakes an API request to generate content using a model. For the `model` parameter, supported formats for Vertex AI API include: - The Gemini model ID, for example: 'gemini-2.0-flash' - The full resource name starts with 'projects/', for example: 'projects/my-project-id/locations/us-central1/publishers/google/models/gemini-2.0-flash' - The partial resource name with 'publishers/', for example: 'publishers/google/models/gemini-2.0-flash' or - `/` separated publisher and model name, for example: 'google/gemini-2.0-flash' For the `model` parameter, supported formats for Gemini API include: - The Gemini model ID, for example: 'gemini-2.0-flash' - The model name starts with 'models/', for example: 'models/gemini-2.0-flash' - For tuned models, the model name starts with 'tunedModels/', for example: 'tunedModels/1234567890123456789' Some models support multimodal input and output. Built-in MCP support is an experimental feature. 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-2.0-flash', 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-2.0-flash', contents=[ types.Part.from_text(text='What is shown in this image?'), types.Part.from_uri(file_uri='gs://generativeai-downloads/images/scones.jpg', mime_type='image/jpeg') ] ) print(response.text) # The image shows a flat lay arrangement of freshly baked blueberry # scones. 6MCP sessions are not supported in synchronous methods.r_rru, Tools at indices [%s] are not compatible with automatic function calling (AFC). AFC is disabled. If AFC is intended, please include python callables in the tool list, and do not include function declaration in the tool list.&AFC is enabled with max remote calls: .r AFC remote call is done.8Reached max remote calls for automatic function calling.userrkrfN)%r "find_afc_incompatible_tool_indexesparse_config_for_mcp_usagerurhas_mcp_session_usagerUnsupportedFunctionErrorshould_disable_afcr isinstancerGenerateContentConfiglendictrjjoinmapstrloggerwarningget_max_remote_calls_afcinforrget_function_mapr'r6rfget_function_response_partsContentrWrYextendlistappendshould_append_afc_history"automatic_function_calling_history)r{rTrVrincompatible_tools_indexes parsed_configoriginal_tools_lengthru indices_strremaining_remote_calls_afcrri function_mapfunc_response_partsfunc_call_contentfunc_response_contentr r r!generate_contentbsB                     ) zModels.generate_contentccst|}t|}|r|jrt|jrtdt|r-|j |||dEdHdS|rrd}t |t j r@|jr?t |j}nt |trQ|dg}|rQt |}t ||kredtt|}td||j |||dEdHdSt|} td| d g} d} d} d} | dkr}| d 7} |j |||d}t|}| d kr|D]1} |st|| }| Vq| jr| jdjr| jdjjsnt| |} | st|| }| Vqn6|D]} t|r| | _t|| }| Vq| dus| jr| jdjr| jdjjsdSt| |} |sdS| sdStd | d | d 8} | dkr.td | durw| jdurw| jdj}t jd| d}t !|}| sS| "|t |t#rh|durh|$||$||durr| $|| $|| dksdSdS)aMakes an API request to generate content using a model and yields the model's response in chunks. For the `model` parameter, supported formats for Vertex AI API include: - The Gemini model ID, for example: 'gemini-2.0-flash' - The full resource name starts with 'projects/', for example: 'projects/my-project-id/locations/us-central1/publishers/google/models/gemini-2.0-flash' - The partial resource name with 'publishers/', for example: 'publishers/google/models/gemini-2.0-flash' or - `/` separated publisher and model name, for example: 'google/gemini-2.0-flash' For the `model` parameter, supported formats for Gemini API include: - The Gemini model ID, for example: 'gemini-2.0-flash' - The model name starts with 'models/', for example: 'models/gemini-2.0-flash' - For tuned models, the model name starts with 'tunedModels/', for example: 'tunedModels/1234567890123456789' Some models support multimodal input and output. Built-in MCP support is an experimental feature. 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' ) for chunk in client.models.generate_content_stream( model='gemini-2.0-flash', contents='''What is a good name for a flower shop that specializes in selling bouquets of dried flowers?''' ): print(chunk.text) # **Elegant & Classic:** # * The Dried Bloom # * Everlasting Florals # * Timeless Petals for chunk in client.models.generate_content_stream( model='gemini-2.0-flash', contents=[ types.Part.from_text('What is shown in this image?'), types.Part.from_uri('gs://generativeai-downloads/images/scones.jpg', 'image/jpeg') ] ): print(chunk.text) # The image shows a flat lay arrangement of freshly baked blueberry # scones. rr_Nrrurz_Tools at indices [%s] are not compatible with automatic function calling. AFC will be disabled.rrr rrrrr)%r rrrurrrrrrrrrrrrjrrrrrrrrappend_chunk_contentsr'r6rfrrrrrWrYrrr)r{rTrVrrrrrurrrchunkrrrrrrr r r!generate_content_streamsB                                zModels.generate_content_streamc Csh|j|||d}d}g}|r|js|S|jD]}|jr%|jjdkr%|j}q||qtj||d}|S)apGenerates images based on a text description and configuration. Args: model (str): The model to use. prompt (str): A text description of the images to generate. config (GenerateImagesConfig): Configuration for generation. Usage: .. code-block:: python response = client.models.generate_images( model='imagen-3.0-generate-002', prompt='Man with a dog', config=types.GenerateImagesConfig( number_of_images= 1, include_rai_reason= True, ) ) response.generated_images[0].image.show() # Shows a man with a dog. rNPositive PromptrrDrrrrrrr r{rTrr api_responserDrgenerated_imagerr r r!generate_imagess,   zModels.generate_imagescCs|j||||dS)aLEdits 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-001', 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. rrr{rTrrrr r r! edit_images 1zModels.edit_imagecCstj||||d|p i}t|tjr|}nt|}tj|dd|dd|dd|dd|dd|dd|d d|d d|d d|d dd }d|d<d|d<|j||||dS)aMakes 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. rrmNrrrrrrrKrNr rmrrrrrrrKrNrupscalerr r rUpscaleImageParametersrUpscaleImageConfigrxr_UpscaleImageAPIConfigDictrjrr{rTrhrQr config_dct api_configr r r! upscale_image0sD!          zModels.upscale_imagec Cs6|s|s|r |r tdi}|jjs;|r;t|tjr|}nt|}|dr:|dr:tj|d|dd}nU|jjs|rt|tj rR|}|di}nt|}t|dtjrk|dt} | }|r|dr|drtj |d|dtj|d|ddd }|j ||||||d S) Generates videos based on an input (text, image, or video) and configuration. The following use cases are supported: 1. Text to video generation. 2a. Image to video generation (additional text prompt is optional). 2b. Image to video generation with frame interpolation (specify last_frame in config). 3. Video extension (additional text prompt is optional) Args: model: The model to use. prompt: The text prompt for generating the videos. Optional for image to video and video extension use cases. This argument is deprecated, please use source instead. image: The input image for generating the videos. Optional if prompt is provided. This argument is deprecated, please use source instead. video: The input video for video extension use cases. Optional if prompt or image is provided. This argument is deprecated, please use source instead. source: The input source for generating the videos (prompt, image, and/or video) config: Configuration for generation. Usage: ``` operation = client.models.generate_videos( model="veo-2.0-generate-001", source=types.GenerateVideosSource( prompt="A neon hologram of a cat driving at top speed", ), ) while not operation.done: time.sleep(10) operation = client.operations.get(operation) operation.result.generated_videos[0].video.uri ``` MSource and prompt/image/video are mutually exclusive. Please only use source.rXrZr-rXr-rirrhrrhrir rrhrirrVideorxrrjGenerateVideosSourcer r{rTrrhrirjr video_dct source_dct video_objr r r!generate_videoszsN1     zModels.generate_videoscCsltj|djp t}|jdurd|_|jjr*|}|js*|j}|r'|dnd|_t d|j |j |d|S)aMakes an API request to list the available models. If `query_base` is set to True in the config or not set (default), the API will return all available base models. If set to False, 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' ... r]NT&filter=labels.tune-type:*labels.tune-type:*r) rrrListModelsConfigrrhricopyrrrr{r filter_valuer r r!rs&   z Models.list)C__name__ __module__ __qualname__rrrContentListUnionContentListUnionDictrGenerateContentConfigOrDictrrrrrEmbedContentConfigOrDictrrGenerateImagesConfigOrDictrrr_ReferenceImageAPIOrDictEditImageConfigOrDictrr ImageOrDict_UpscaleImageAPIConfigOrDictrrRecontextImageSourceOrDictRecontextImageConfigOrDictrrSegmentImageSourceOrDictSegmentImageConfigOrDictrrGetModelConfigOrDictrrjListModelsConfigOrDictrrUpdateModelConfigOrDictrDeleteModelConfigOrDictrrCountTokensConfigOrDictrrComputeTokensConfigOrDictrr VideoOrDictGenerateVideosSourceOrDictGenerateVideosConfigOrDictrrrrrrUpscaleImageConfigOrDictrrrr r r r!r\s R W b N F E o T > G F I ` W Q ' D = > N `r\c@s<eZdZdddedeejejfdeej dej fddZ dddedeejejfdeej de e ej fd d Zdddedeejejfdeejdejfd d Zddded edeejdejfddZddded edeejdeejdejf ddZdddedejdedeejdejf ddZdddedejdeejdej fddZ!dddedej"deej#dej$fddZ%dddedeej&dej'fddZ(dddeej)dej*fddZ+dddedeej,dej'fd d!Z-dddedeej.dej/fd"d#Z0dddedeejejfdeej1dej2fd$d%Z3dddedeejejfdeej4dej5fd&d'Z6dddddd(ded eedeejd)eej7deej8deej9dej:fd*d+Z;dddedeejejfdeej dej fd,d-Zdddeej)de?ej'fd2d3Zddded edeejdejfd4d5Z@dddedejdedeejAdejf d6d7ZBdddddd(ded eedeejd)eej7deej8deej9dej:fd8d9ZCdS): AsyncModelsNr]rTrVrrc stj|||d}|jjr#t|j|}|d}|r d|}nd}nt|j|}|d}|r6d|}nd}|d}|rH|dt|}| ddd} |j dur_|j j dur_|j j } t |}t |}|jd||| IdH} |durt|ddrtj| d } |j| | S| jsint| j} |jjrt| } |jjst| } tjj| |d } tj| jd | _|j| | Sr^)rrgrhrir&rjrkr%rrlrrmr rnro async_requestrqrrrsrtrurvr3r0rwrxryrfr]rzr r r!r sj              zAsyncModels._generate_contentc stj|||djjr#tj}|d}|r d|}nd}ntj}|d}|r6d|}nd}|d}|rH|dt|}| ddd}j dur_j j dur_j j }t |}t |}|durwt|ddrwtdjd |||IdHfd d } | S) Nr_rUrrr`rrbrracs2z<3dHW}|jsint|j}jjrt|}jjs$t|}tjj | d}tj |j d|_ j||Vq6dS)Nrcre)rtrurvrhrir3r0rrrrwrxryrfr]rs)rrrr|response_streamr{r r!async_generators  z=AsyncModels._generate_content_stream..async_generator)rrgrhrir&rjrkr%rrlrrmr rnrorqrasync_request_streamed) r{rTrVrr}r~rrrmrCr rAr!rYs\          z$AsyncModels._generate_content_streamc sTtj|||d}|jjr#t|j|}|d}|r d|}nd}nt|j|}|d}|r6d|}nd}|d}|rH|dt|}| ddd} |j dur_|j j dur_|j j } t |}t |}|jd ||| IdH} | jszint| j} |jjrt| } |jjst| } tjj| |d } tj| jd | _|j| | S) aKCalculates embeddings for the given contents. Only text is supported. Args: model (str): The model to use. contents (list[Content]): The contents to embed. config (EmbedContentConfig): Optional configuration for embeddings. Usage: .. code-block:: python embeddings = await client.aio.models.embed_content( model= 'text-embedding-004', contents=[ 'What is your name?', 'What is your favorite color?', ], config={ 'output_dimensionality': 64 }, ) r_rUrrrr`rNrarcre)rrrhrirrjrkrrrlrrmr rnror@rtrurvrrrrwrxryrfr]rsrr r r!rs^            zAsyncModels.embed_contentrc Ttj|||d}|jjr#t|j|}|d}|r d|}nd}nt|j|}|d}|r6d|}nd}|d}|rH|dt|}| ddd} |j dur_|j j dur_|j j } t |}t |}|jd||| IdH} | jszint| j} |jjrt| } |jjst| } tjj| |d } tj| jd | _|j| | S) z4Private method for generating images asynchronously.rrUrrr`rNrarcre)rrrhrir@rjrkr>rrlrrmr rnror@rtrurvrIrFrrwrxryrfr]rsrr r r!r s^             zAsyncModels._generate_imagesrc"tj||||d}|jjstdt|j|}|d}|r%d|}nd}|d} | r7|dt| }| ddd} |j durN|j j durN|j j } t |}t |}|jd ||| IdH} | jsiint| j} |jjrwt| } tjj| |d } tj| jd | _|j| | S) z3Private method for editing an image asynchronously.rrrUrrr`rNrarcre)rrrhrirrrjrkrrlrrmr rnror@rtrurvrrrwrxryrfr]rsrr r r!rUP           zAsyncModels._edit_imagerhrQcrF) z5Private method for upscaling an image asynchronously.rrrUrrr`rNrarcre)rrrhrirrSrjrkrrlrrmr rnror@rtrurvrTrrwrxryrfr]rsrr r r!rrGzAsyncModels._upscale_imagerjc tj|||d}|jjstdt|j|}|d}|r$d|}nd}|d}|r6|dt|}| ddd} |j durM|j j durM|j j } t |}t |}|jd ||| IdH} | jshint| j} |jjrvt| } tjj| |d } |j| | Sr)rrrhrirrrjrkrrlrrmr rnror@rtrurvrrrwrxrsrr r r!rsH7          zAsyncModels.recontext_imagec rH) aSegments an image, creating a mask of a specified area. Args: model (str): The model to use. source (SegmentImageSource): An object containing the source inputs (prompt, image, scribble_image) for image segmentation. The prompt is required for prompt mode and semantic mode, disallowed for other modes. scribble_image is required for the interactive mode, disallowed for other modes. config (SegmentImageConfig): Configuration for segmentation. Usage: ``` response = client.models.segment_image( model="image-segmentation-001", source=types.SegmentImageSource( image=types.Image.from_file(IMAGE_FILE_PATH), ), config=types.SegmentImageConfig( mode=types.SegmentMode.foreground, ), ) mask_image = response.generated_masks[0].mask ``` rrrUrrr`rNrarc)rrrhrirr#rjrkrrlrrmr rnror@rtrurvr%rrwrxrsrr r r!r?sH#          zAsyncModels.segment_imagec sBtj||d}|jjr"t|j|}|d}|rd|}nd}nt|j|}|d}|r5d|}nd}|d}|rG|dt|}| ddd}|j dur^|j j dur^|j j }t |}t |}|jd|||IdH} | jsyint| j} |jjrt| } |jjst| } tjj| |d} |j| | Sr)rrrhrirrjrkrrrlrrmr rnror@rtrurvrrrrwrxrsrr r r!rjsV            zAsyncModels.getc stj|d}|jjr!t|j|}|d}|rd|}nd}nt|j|}|d}|r4d|}nd}|d}|rZ|drZ|d}|d|}|rY|dt |7}n |re|dt |}|d dd}|j dur||j j dur||j j }t |}t |}|jd |||IdH} | jsint| j} |jjrt| } |jjst| } tjj| |d } tj| jd | _|j| | Sr)rrrhrirrjrkrrlrrrmr rnror@rtrurvrrrrwrxryrfr]rsrr r r!rsf             zAsyncModels._listc sBtj||d}|jjr"t|j|}|d}|rd|}nd}nt|j|}|d}|r5d|}nd}|d}|rG|dt|}| ddd}|j dur^|j j dur^|j j }t |}t |}|jd|||IdH} | jsyint| j} |jjrt| } |jjst| } tjj| |d } |j| | Sr)rrrhrirJrjrkrHrrlrrmr rnror@rtrurvrrrrwrxrsrr r r!rsV            zAsyncModels.updatec sRtj||d}|jjr"t|j|}|d}|rd|}nd}nt|j|}|d}|r5d|}nd}|d}|rG|dt|}| ddd}|j dur^|j j dur^|j j }t |}t |}|jd|||IdH} | jsyint| j} |jjrt| } |jjst| } tjj| |d} tj| jd | _|j| | Sr)rrrhrirrjrkrrrlrrmr rnror@rtrurvrrrrwrxryrfr]rsrr r r!r[s\            zAsyncModels.deletec rE) aBCounts the number of tokens in the given content. Multimodal input is supported for Gemini models. Args: model (str): The model to use for counting tokens. contents (list[types.Content]): The content to count tokens for. config (CountTokensConfig): The configuration for counting tokens. Usage: .. code-block:: python response = await client.aio.models.count_tokens( model='gemini-2.0-flash', contents='What is your name?', ) print(response) # total_tokens=5 cached_content_token_count=None r_rUrrr`rNrarcre)rrrhrirrjrkrrrlrrmr rnror@rtrurvrrrrwrxryrfr]rsrr r r!rs^            zAsyncModels.count_tokensc s tj|||d}|jjstdt|j|}|d}|r$d|}nd}|d}|r6|dt|}| ddd} |j durM|j j durM|j j } t |}t |}|jd ||| IdH} | jshint| j} |jjrvt| } tjj| |d } tj| jd | _|j| | S) aFGiven a list of contents, returns a corresponding TokensInfo containing the list of tokens and list of token ids. Args: model (str): The model to use. contents (list[shared.Content]): The content to compute tokens for. Usage: .. code-block:: python response = await client.aio.models.compute_tokens( model='gemini-2.0-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'?'])] r_rrUrrr`rNrarcre)rrrhrirr[rjrkrrlrrmr rnror@rtrurvr`rrwrxryrfr]rsrr r r!rsN          zAsyncModels.compute_tokensrricsJtj||||||d}|jjr&t|j|}|d} | r#d| } nd} nt|j|}|d} | r9d| } nd} |d} | rK| dt| } | ddd} |j durb|j j durb|j j } t |}t |}|jd| || IdH} | js}int| j}|jjrt|}|jjst|}tjj||d }|j||S) z4Private method for generating videos asynchronously.rrUrrr`rNrarc)rrrhrirqrjrkrmrrlrrmr rnror@rtrurvrercrrwrxrsrr r r!rLs^              zAsyncModels._generate_videoscst|}t|IdH\}}t|r |j|||dIdHS|rcd}t|tjr3|jr2t |j}nt|t rD| dg}|rDt |}t ||krXd t t|} td| |j|||dIdHSt|} td| dg} t} | dkr|j|||dIdH} | d 8} | dkrtd tj||d d } | snV| snS| jr| jdjr| jdjjsnBt| | IdH}|sn6| jdj}tjd |d}t|}| s| |t|tr||||| || || dks{t|r| dur| | _ | S)adMakes an API request to generate content using a model. Some models support multimodal input and output. Built-in MCP support is an experimental feature. 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-2.0-flash', 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. Nr_rrurrrrr rTis_caller_method_asyncrr)!r rparse_config_for_mcp_sessionsrrrrrrurrrjrrrrrrrrrrr'r6rf!get_function_response_parts_asyncrrWrYrrrrr)r{rTrVrrrmcp_to_genai_tool_adaptersrrurrrrrrrrr r r!rs(                     + zAsyncModels.generate_contentc st|}t|IdH\}t|r,j|||dIdHfdd}||||S|r{d}t|tjr?|jr>t |j}nt|t rP| dg}|rPt |}t ||krdd t t|} td| j|||dIdHfd d}||||Sfd d } | |||S) a2Makes an API request to generate content using a model and yields the model's response in chunks. For the `model` parameter, supported formats for Vertex AI API include: - The Gemini model ID, for example: 'gemini-2.0-flash' - The full resource name starts with 'projects/', for example: 'projects/my-project-id/locations/us-central1/publishers/google/models/gemini-2.0-flash' - The partial resource name with 'publishers/', for example: 'publishers/google/models/gemini-2.0-flash' or - `/` separated publisher and model name, for example: 'google/gemini-2.0-flash' For the `model` parameter, supported formats for Gemini API include: - The Gemini model ID, for example: 'gemini-2.0-flash' - The model name starts with 'models/', for example: 'models/gemini-2.0-flash' - For tuned models, the model name starts with 'tunedModels/', for example: 'tunedModels/1234567890123456789' Some models support multimodal input and output. Built-in MCP support is an experimental feature. 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' ) async for chunk in await client.aio.models.generate_content_stream( model='gemini-2.0-flash', contents='''What is a good name for a flower shop that specializes in selling bouquets of dried flowers?''' ): print(chunk.text) # **Elegant & Classic:** # * The Dried Bloom # * Everlasting Florals # * Timeless Petals async for chunk in await client.aio.models.generate_content_stream( model='gemini-2.0-flash', contents=[ types.Part.from_text('What is shown in this image?'), types.Part.from_uri('gs://generativeai-downloads/images/scones.jpg', 'image/jpeg') ] ): print(chunk.text) # The image shows a flat lay arrangement of freshly baked blueberry # scones. Nr_c 2z 3dHW}|Vq6dSNr rTrVrrrr r!base_async_generatorgzAAsyncModels.generate_content_stream..base_async_generatorrrurrcrNrOr rPrQr r!rRrSc s<t|}td|dg}d}d}d}|dkr|d7}j|||dIdH}|dkr8td|d|d8}|dkrI|dkrItdtj|d d } |dkr|2z83dHW}| sit||}|VqW|jry|jdjry|jdjj s{nt || IdH}|st||}|VqW6n:|2z3dHW}t |r||_ t||}|Vq6|dus|jr|jdjr|jdjj sdSt || IdH}| sdS|sdS|durq|jdj} t jd |d } t|}|s||t|tr| dur|| || | dur|| || |dksdSdS) Nrrrr r_rrrTrIrr)r rrrrrrr'r6rfrLrrrrrWrYrrrr) rTrVrrrrrrrrrr)rMr{r r!rCs                     z.async_generator)r rrKrrrrrrurrrjrrrrr) r{rTrVrrrrRrrurrCr )rMrr{r!rsDC           az#AsyncModels.generate_content_streamcs|j||||dIdHS)aVEdits 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 = await client.aio.models.edit_image( model='imagen-3.0-capability-001', 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. rNrrr r r!rs1 zAsyncModels.edit_imagecsttj|djp t}|jdurd|_|jjr+|}|js+|j}|r(|dnd|_t d|j |j |dIdH|S)aMakes an API request to list the available models. If `query_base` is set to True in the config or not set (default), the API will return all available base models. If set to False, 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' ... r]NTrr r) rrrr!rrhrir"rrrr#r r r!r$s(  zAsyncModels.listc sp|j|||dIdH}d}g}|r|js|S|jD]}|jr)|jjdkr)|j}q||qtj||d}|S)azGenerates images based on a text description and configuration. Args: model (str): The model to use. prompt (str): A text description of the images to generate. config (GenerateImagesConfig): Configuration for generation. Usage: .. code-block:: python response = await client.aio.models.generate_images( model='imagen-3.0-generate-002', prompt='Man with a dog', config=types.GenerateImagesConfig( number_of_images= 1, include_rai_reason= True, ) ) response.generated_images[0].image.show() # Shows a man with a dog. rNrrrrr r r!rXs.    zAsyncModels.generate_imagescstj||||d|p i}t|tjr|}nt|}tj|dd|dd|dd|dd|dd|dd|d d|d d|d d|d dd }d|d<d|d<|j||||dIdHS)aMakes 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. rrmNrrrrrrrKrNrr r rr rr rr r r!rsF!           zAsyncModels.upscale_imagec s>|s|s|r |r tdi}|jjs<|rrrr r r r!r? s R  \ b N F E o W @ I F I ` V Q  Z ; 9 = N r?rO)ruloggingtypingrrrrrr urllib.parserr r rr r rrrWrrrhrrr0rr1pagersrr getLoggerrr"r%r'rrobjectr4rOrNr[r`rbrermrsrwr}rrrrrrrrrrrrrrrrrrrrrr$r%r&r0r3r9r;r>r@rFrIrWr]rcrermrqrbrdrlrprrBrrur|r|rrrrrrrVr[rrrrrrrrrrhrrrrrrrErHrrr!r#r%r"r#rryrrErGrHrJrPrSrTr\rSrZrrrkro BaseModuler\r?r r r r!s                    :                  &                   {  "    "  +  '  !          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