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[8*%y+  s5B7 c bi}t|dg.t|ddgtj|t|dgt|dg:t|dgtjt|dgDcgc]}|c}t|dgt t|dg|||Scc}wr)r7r8rcrdrerrfs r! _CountTokensParameters_to_vertexrs !) +y!-  *d; :;  + |$0  ,,tK*'FGH$H  +z".  [8*%y+  Is5 B, ci}t|dgt|dgt|dgt|dgt|dgt|dgt|dgt|dgt|dg|S)Nrirj totalTokens total_tokenscachedContentTokenCountcached_content_token_countr]r9s r!_CountTokensResponse_from_mldevrs !) +)*+7'(${=N*+  +()*6 ' [+,-  +x ,v.[6(0KL +-./; ' [012  ++,-9 )* [./0  +345A 01 [678  +,-.: )* [/01  + |$0 z" [:,'  +*+,83 [-./  +456B= [789  +()5 ~& [?+,  +z". Dxj$AB + }%1 z" [;-(  + ~&21 [<.) r#c i}t|dg.t|ddgtj|t|dgt|dgt|ddgt|dgt|dg3t|ddgt|dgDcgc]}t |||c}t|dgt t|dg|||Scc}w)Nr`raprompt instances[0]reference_imagesreferenceImagesr)r7r8rcrd_ReferenceImageAPI_to_vertexrrfs r!_EditImageParameters_to_vertexrs !) +y!-  *d; :;  +z".^X.[8*0MN +*+,8 *+[+=*>?  )y+ F  +z". 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[456  ++,-9  [./0  + |$0ZL${ZL"AB ++,-9%&[;N:O(P +*+,8%&[;M:N(O +*+,8%&[;M:N(O ++,-9  [./0  +,-.:  [/01  +-./;  [012  +)*+7%&[;L:M(NO +()*6_%tK:J9K'LM +x ,VHd;9: +()5^$d;8I&JK +()*6_%tK:J9K'LM + '3]OT+ %GH +)*+7%&[;L:M(NO +y!-VHd; :; +y!-VHd; :; +789E P  r#c i}t|dg.t|ddgtj|t|dg|Srrrs r!_GetModelParameters_to_mldevr rr#c i}t|dg.t|ddgtj|t|dg|Srrrs r!_GetModelParameters_to_vertexr rr#c i}t|dg$t|dgtt|dg||t|dgt|dgt|dg|S)N auth_config authConfig enable_widget enableWidget)r7r8r;r9s r!_GoogleMaps_to_mldevr sq !) + '3  }o . ;  +()5^$d;8I&JK r#ci}t|dgt|dgt|dgt|dg tdt|dg tdt|dgt|dgt|dg|S) N search_types searchTypesblocking_confidencez=blocking_confidence parameter is not supported in Gemini API.exclude_domainsz9exclude_domains parameter is not supported in Gemini API.time_range_filtertimeRangeFilterr6r9s r!_GoogleSearch_to_mldevr s !) +'(4]OT+7G%HI +-./; G  +)*+7 C  ++,-9%&[;N:O(P r#ci}t|dgt|dgt|dgt|dgt|dgt|dgt|dg tdt|dg tdt|d g td t|d g td t|d g td|S)Nrrrs imageSizerz;person_generation parameter is not supported in Gemini API.prominent_peoplez:prominent_people parameter is not supported in Gemini API.rz:output_mime_type parameter is not supported in Gemini API.rzDoutput_compression_quality parameter is not supported in Gemini API.image_output_optionsz>image_output_options parameter is not supported in Gemini API.r6r9s r!rTrT s  !) +'(4]OT+7G%HI + ~&2[M4 l^#DE ++,-9 E  +*+,8 D  +*+,8 D  +456B N  +./0< H  r#c"i}t|dgt|dgt|dgt|dgt|dgt|dgt|dgt|dgt|dgt|dgt|dgt|dgt|d gt|d d gt|d gt|d gt|d d gt|d gt|dgt|d gt|dg|S)NrrrsrrrrprominentPeoplerimageOutputOptionsrArrrr]r9s r!r\r\ s[ !) +'(4]OT+7G%HI + ~&2[M4 l^#DE ++,-9  [./0  +*+,8%&[;M:N(O +*+,8 z* [-./  +456B 34 [789  +./0<  [123 r#c i}t|dg,t|dgtjt|dgt|dgt|dgt|dg|S)NbytesBase64Encoded image_bytesrAr@r7r8base_tt_bytesr9s r!rr= so !) +,-.: tK*>)?@A  + |$0[M4 j\#BC r#c i}t|dgt|dgt|dgt|dg,t|dgtjt|dgt|dgt|dgt|dg|S)NgcsUrigcs_urirrrAr@rr9s r!rrQ s !) +z".YKkH:!>? +,-.: tK*>)?@A  + |$0[M4 j\#BC r#c i}t|dg tdt|dg,t|dgtjt|dgt|dgt|dgt|dg|S)Nrz1gcs_uri parameter is not supported in Gemini API.rrr@rA)r7rr8rrr9s r!rrg s !) + {#/ H II + '3 tK-9:  + }%1ZL${[M"BC r#c i}t|dgt|dgt|dgt|dg,t|dgtjt|dgt|dgt|dgt|dg|S)Nrrrrr@rArr9s r!rr} s !) + {#/XJ[9+ >? + '3 tK-9:  + }%1ZL${[M"BC r#c ji}t|dgt|ddgt|dgt|dgt|ddgt|dgt|dgt|ddgt|dgt|dg.t|dd gtj|t|dg|S N page_size_querypageSize page_token pageTokenfilter query_basera models_urlr7r8rc t_models_urlrs r!_ListModelsConfig_to_mldevr !) + }%1*-tK+/O + ~&2 ; [<.)  +z".8,d; .KL + ~&2  z4 l^#DE r#c ji}t|dgt|ddgt|dgt|dgt|ddgt|dgt|dgt|ddgt|dgt|dg.t|dd gtj|t|dg|Srrrs r!_ListModelsConfig_to_vertexr  rr#cVi}t|dgt|t|dg|||SNr)r7rrs r!_ListModelsParameters_to_mldevr  s: !) +z".Dxj19k r#cVi}t|dgt|t|dg|||Sr )r7r rs r!_ListModelsParameters_to_vertexr s: !) +z".Dxj19k r#c Pi}t|dgt|dgt|dgt|dgt|dgt|dgt|dgEt|dgtjt|dgDcgc]}t |||c}|Scc}wNrirj nextPageTokennext_page_tokenrmodels)r7r8rct_extract_models_Model_from_mldevrWs r!_ListModelsResponse_from_mldevr s !) +)*+7'(${=Nrversionrtuned_model_infoinputTokenLimitinput_token_limitoutputTokenLimitoutput_token_limitsupportedGenerationMethodssupported_actionsrmaxTemperaturemax_temperaturer!r r#r"thinkingr]r9s r!rr= s) !) +x ,VHd;9: + '3^$d;&HI + '3]OT+ %GH + {#/YKkI;!?@ +y!-'(${WI*FG +)*+7'(${=Nr versionIdr&deployedModels endpointsrrr'defaultCheckpointIddefault_checkpoint_id checkpoints)r7r8r_TunedModelInfo_from_vertexrWs r!rrw s !) +x ,VHd;9: + '3^$d;&HI + '3]OT+ %GH + }%1YKkK=!AB +()*6  [+;*<=  "$ ; ?  +z".XJ[8* => +y!- # wi ()[  +-./; ! [012  + '3 {]O<=$= A : >s ?F ; F c i}t|dgt|dgt|dgt|dgt|dgt|dgt|dgt|dgt|dgt|dg$t|dgtt|dg||t|d g$t|d gtt|d g||t|d gt|d gt|d gt|d g$t|dgt t|d g||t|dgt|dgt|dgt|dgt|dgt|dgt|dgt|dgt|dgt|dgt|dgt|dg|S)Nr@rAcode_execution_resultcodeExecutionResultexecutable_codeexecutableCode file_datafileData function_call functionCallfunction_responsefunctionResponse inline_data inlineDatatextthoughtthought_signaturethoughtSignaturevideo_metadata videoMetadata)r7r8rrrBr9s r!rxrx s' !) +*+,8%&[;M:N(O +/01=  [234  +)*+7%&[;L:M(NO + }%1   {m ,i  +()5  / 0)[  ++,-9  [./0  + '3  }o . ;  +x ,VHd;9: + {#/YKkI;!?@ ++,-9  [./0  +()*6_%tK:J9K'LM r#c li}t|dg$t|dgtt|dg|||S)N product_imagerr7r8rr9s r!_ProductImage_to_vertexrO sJ !) +()5   / 0)[  r#cbi}t|dgt|ddgt|dgt|dgt|ddgt|dgt|dgt|ddgt|dgt|dgt|ddgt|dgt|d gt|dd gt|d gt|d gt|dd gt|d gt|d gt|ddgt|d gt|dgt|gdt|dgt|dgt|gdt|dgt|dgt|ddgt|dgt|dgt|dgt|dg|S)Nrrrrrrrrrrrrrrrrrrrurxrr]r9s r!_RecontextImageConfig_to_vertexrQ s# !) +*+,8 }% [-./  + ~&2 {# [<.)  +()*6 |$ [+,-  +x ,v.[6(0KL +-./; ' [012  ++,-9 )* [./0  +()5 ~& [?+,  +*+,83 [-./  +456B= [789  +()*6 ' [+,-  +z". Dxj$AB r#c i}t|dg.t|ddgtj|t|dgt|dgt t|dg||t|dgt t|dg|||SNr`rarr)r7r8rcrd_RecontextImageSource_to_vertexrQrs r!#_RecontextImageParameters_to_vertexrU[ s !) +y!-  *d; :;  +z".# [8*%y+ +z".# [8*%y+ r#c i}t|dg2t|dgt|dgDcgc]}t|||c}|Scc}w)NrrrrWs r!#_RecontextImageResponse_from_vertexrWv s` !) + '3 [=/:  (i E    A c Fi}t|dgt|ddgt|dgt|dg%t|gdtt|dg||t|dg3t|ddgt|dgDcgc]}t|||c}|Scc}w)Nrr person_image)r personImagerproduct_images productImages)r7r8rrOrWs r!rTrT s !) +z".14 hZ3P +'(40 ~. /K  +()*6 )[+;*<=  $D)[ A    sB c `i}t|dg$t|dgtt|dg||t|dgt|dgt|dgt|dgt|dgt|dgt|dg$t|dgtt|dg||t|d g$t|d gt t|d g||t|d gt|d gt|d gt|d gt|dgt|d g|S)Nreference_imagereferenceImage reference_id referenceIdreference_type referenceTypemask_image_configmaskImageConfigcontrol_image_configcontrolImageConfigstyle_image_configstyleImageConfigsubject_image_configsubjectImageConfig)r7r8rr#rr9s r!rr s !) +)*+7  01 2I{  +'(4]OT+7G%HI +()*6_%tK:J9K'LM ++,-9 & 23 4i  +./0< ) 56 7K  +,-.:  [/01  +./0<  [123 r#c i}t|ddgt|dgt|ddgt|ddgt|dgt|ddgt|dgt|dgt|dg|SNsafetyAttributes categoriesscores contentType content_typer]r9s r!rr !) +*L9:F  [-|<=  +*H56BH:tK2Dh1OP + '3^$d;&HI r#c i}t|ddgt|dgt|ddgt|ddgt|dgt|ddgt|dgt|dgt|dg|Srnr]r9s r!rr rtr#ci}t|dgt|dgt|dgt|dg tdt|dgt|dgt|dg|S)Ncategorymethodz0method parameter is not supported in Gemini API. thresholdr6r9s r!rMrMsv !) + |$0ZL${ZL"AB +z". 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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`rbrDrrNra{model}:embedContent{model}:predict{model}:batchEmbedContentsrrrrrr)r_EmbedContentParametersPrivaterrrrrct_is_vertex_embed_content_modelr`rrr rrrr rrrrrrrrEmbedContentResponserrrrrjrrr`rbrDrrrrr endpoint_urlrrrrrrs r!_embed_contentzModels._embed_contentsKH::- O   =   OT?l&))&1/0/P/PQ`QfQf/g+m~l &&'78<   OT?l&))&1 +667GH+##H-LvQy./ 0dXt$04L*  " " / / ;$++88l**<8L66|DL''lLH'mmBHMM1JM   7 m    $ $6 m--<<'A'A'C=L&+%7%7  &L" %%l3 r#rctj|||}|jjr@t |j|d|}|j d}|rdj |}nBd}n?t|j|d|}|j d}|rdj |}nd}|j d}|r|dt|}|jddd} |j,|jj|jj} tj|}tj|}|jjd||| } | j sint#j$| j } |jjr t'| d|} |jjs t)| d|} tj*j-| |j/ } tj0| j2 | _|jj7| | S) z%Private method for generating images.r`rrNrarrrrrrr)r_GenerateImagesParametersrrr~rrr|r rrrr rrrrrrrrGenerateImagesResponserrrrrjr rr`rrrrrrrrrrrs r!_generate_imageszModels._generate_imagess'55O   8   OT?l&))&1  ++,<= 7   OT?l&))&1  ++,<= ##H-LvQy./ 0dXt$04L*  " " / / ;$++88l**<8L66|DL''lLH'mmBHMM1JM   9 m    $ $8 m//>>'A'A'C?L&+%7%7  &L" %%l3 r#rctj||||}|jjs t dt |j|d|}|j d}|rdj|}nd}|j d} | r|dt| }|jddd} |j,|jj|jj} tj|}tj|}|jjd ||| } | j sint#j$| j } |jjr t'| d|} tj(j+| |j- } tj.| j0 | _|jj5| | S) z$Private method for editing an image.r`rrr6This method is only supported in the Vertex AI client.Nrarrrrrrr)r_EditImageParametersrrrrrrr rrrr rrrrrrrEditImageResponserrrrrjrrr`rrrrrrrrrrrrs r! _edit_imagezModels._edit_image?s00) O    $ $ O PP3   OT?l&))&1  ++,<= ##H-LvQy./ 0dXt$04L*  " " / / ;$++88l**<8L66|DL''lLH'mmBHMM1JM   4 m**99'A'A'C:L&+%7%7  &L" %%l3 r#rrctj||||}|jjs t dt |j|d|}|j d}|rdj|}nd}|j d} | r|dt| }|jddd} |j,|jj|jj} tj|}tj|}|jjd ||| } | j sint#j$| j } |jjr t'| d|} tj(j+| |j- } tj.| j0 | _|jj5| | S) z&Private method for upscaling an image.r`rrrrNrarrrrrrr)r_UpscaleImageAPIParametersrrrrrrr rrrr rrrrrrrUpscaleImageResponserrrrrjrrr`rrrrrrrrrrrrs r!_upscale_imagezModels._upscale_images66% O    $ $ O PP9   OT?l&))&1  ++,<= ##H-LvQy./ 0dXt$04L*  " " / / ;$++88l**<8L66|DL''lLH'mmBHMM1JM   7 m--<<'A'A'C=L&+%7%7  &L" %%l3 r#rctj|||}|jjs t dt |j|d|}|j d}|rdj|}nd}|j d}|r|dt|}|jddd} |j,|jj|jj} tj|}tj|}|jjd ||| } | j sint#j$| j } |jjr t'| d|} tj(j+| |j- } |jj/| | 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-001", 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 ``` r`rrrNrarrrrrr)r_RecontextImageParametersrrrrUrrr rrrr rrrrrrrWRecontextImageResponserrr rr`rrrrrrrrrrrs r!recontext_imagezModels.recontext_imagesn55O    $ $ O PP8   OT?l&))&1  ++,<= ##H-LvQy./ 0dXt$04L*  " " / / ;$++88l**<8L66|DL''lLH'mmBHMM1JM   9 m//>>'A'A'C?L %%l3 r#ctj|||}|jjs t dt |j|d|}|j d}|rdj|}nd}|j d}|r|dt|}|jddd} |j,|jj|jj} tj|}tj|}|jjd ||| } | j sint#j$| j } |jjr t'| d|} tj(j+| |j- } |jj/| | S) 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 ``` r#rNrarrrrrr)r_SegmentImageParametersrrrrrrr rrrr rrrrrrrSegmentImageResponserrrr&s r! segment_imagezModels.segment_image/s@33O    $ $ O PP6   OT?l&))&1  ++,<= ##H-LvQy./ 0dXt$04L*  " " / / ;$++88l**<8L66|DL''lLH'mmBHMM1JM   7 m--<<'A'A'C=L %%l3 r#cFtj||}|jjr@t |j|d|}|j d}|rdj |}nBd}n?t|j|d|}|j d}|rdj |}nd}|j d}|r|dt|}|jddd}|j,|jj|jj}tj|}tj|}|jjd|||} | j sint#j$| j } |jjr t'| d|} |jjs t)| d|} tj*j-| |j/} |jj1| | S Nr`rra{name}rrrrr)r_GetModelParametersrrrrrrr rrrr rrrrrrrrModelrrr rr`rrrrrrrrrrs r!rz Models.gets//O   2   OT?l&))&1 ""#341   OT?l&))&1 ""#34##H-LvQy./ 0dXt$04L*  " " / / ;$++88l**<8L66|DL''t\<PH&mmBHMM1JM   (oNm    $ $' t_Mm;;--'A'A'C.L %%l3 r#c tj|}|jjr@t |j|d|}|j d}|rdj |}nBd}n?t|j|d|}|j d}|rdj |}nd}|j d}|r=|j dr,|jd}|d|}|r$|dt|z }n|r|dt|}|jd dd}|j,|jj|jj}tj|}tj|}|jjd |||} | j sint#j$| j } |jjr t'| d|} |jjs t)| d|} tj*j-| |j/ } tj0| j2 | _|jj7| | S Nrraz {models_url}rrz?filter=&rrrrr)r_ListModelsParametersrrrrrr rr rrr rrrrrrrrListModelsResponserrrrrjr rrrrrrrquery_param_filterrrrrs r!_listz Models._listsi11O    4   OT?l&))&1 (()9:3   OT?l&))&1 (()9:##H-L ((2'++H5vX01 2d  !Il+,-- vQy./ 0dXt$04L*  " " / / ;$++88l**<8L66|DL''t\<PH&mmBHMM1JM   5 m    $ $4 m++::'A'A'C;L&+%7%7  &L" %%l3 r#cFtj||}|jjr@t |j|d|}|j d}|rdj |}nBd}n?t|j|d|}|j d}|rdj |}nd}|j d}|r|dt|}|jddd}|j,|jj|jj}tj|}tj|}|jjd|||} | j sint#j$| j } |jjr t'| d|} |jjs t)| d|} tj*j-| |j/ } |jj1| | S Nr.raz{model}r/rrrpatchr)r_UpdateModelParametersrrrrrrr rrrr rrrrrrrrr1rrrr2s r!updatez Models.updates 22O   5   OT?l&))&1 ##$454   OT?l&))&1 ""#34##H-LvQy./ 0dXt$04L*  " " / / ;$++88l**<8L66|DL''|\H'mmBHMM1JM   (oNm    $ $' t_Mm;;--'A'A'C.L %%l3 r#ctj||}|jjr@t |j|d|}|j d}|rdj |}nBd}n?t|j|d|}|j d}|rdj |}nd}|j d}|r|dt|}|jddd}|j,|jj|jj}tj|}tj|}|jjd|||} | j sint#j$| j } |jjr t'| d|} |jjs t)| d|} tj*j-| |j/} tj0| j2 | _|jj7| | S Nr.rar/rrrdeleterr)r_DeleteModelParametersrrrrrrr rrrr rrrrrrrrDeleteModelResponserrrrrjrr2s r!rBz Models.deleteJs$ 22O   5   OT?l&))&1 ""#344   OT?l&))&1 ""#34##H-LvQy./ 0dXt$04L*  " " / / ;$++88l**<8L66|DL''$ lH'mmBHMM1JM   6 m    $ $5 m,,;;'A'A'C<L&+%7%7  &L" %%l3 r#ctj|||}|jjr@t |j|d|}|j d}|rdj |}nBd}n?t|j|d|}|j d}|rdj |}nd}|j d}|r|dt|}|jddd} |j,|jj|jj} tj|}tj|}|jjd||| } | j sint#j$| j } |jjr t'| d|} |jjs t)| d|} tj*j-| |j/ } tj0| j2 | _|jj7| | S) 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 rNra{model}:countTokensrrrrrr)r_CountTokensParametersrrrrrrr rrrr rrrrrrrrCountTokensResponserrrrrjrrs r! count_tokenszModels.count_tokenss'822O   5   OT?l&))&1 $//0@A$4   OT?l&))&1 $//0@A$##H-LvQy./ 0dXt$04L*  " " / / ;$++88l**<8L66|DL''lLH'mmBHMM1JM   6 m    $ $5 m,,;;'A'A'C<L&+%7%7  &L" %%l3 r#ctj|||}|jjs t dt |j|d|}|j d}|rdj|}nd}|j d}|r|dt|}|jddd} |j,|jj|jj} tj|}tj|}|jjd ||| } | j sint#j$| j } |jjr t'| d|} tj(j+| |j- } tj.| j0 | _|jj5| | 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'?'])] rrNra{model}:computeTokensrrrrrr)r_ComputeTokensParametersrrrrgrrr rrrr rrrrrrrmComputeTokensResponserrrrrjrrs r!compute_tokenszModels.compute_tokenss<44O    $ $ O PP7   OT?l&))&1 &112BC&##H-LvQy./ 0dXt$04L*  " " / / ;$++88l**<8L66|DL''lLH'mmBHMM1JM   8 m..=='A'A'C>L&+%7%7  &L" %%l3 r#rrrrrrcNtj||||||}|jjr@t |j|d|}|j d} | rdj | } nBd} n?t|j|d|}|j d} | rdj | } nd} |j d} | r| dt| } |jddd} |j,|jj|jj} tj|}tj|}|jjd| || } | j sint#j$| j }|jjr t'|d|}|jjs t)|d|}tj*j-||j/ }|jj1||S) z%Private method for generating videos.r`rrrrrNra{model}:predictLongRunningrrrrr)r_GenerateVideosParametersrrrrrrr rrrr rrrrrrrrGenerateVideosOperationrrrrr`rrrrrrrrrrrrrrs r!_generate_videoszModels._generate_videosFs55 O   8   OT?l&))&1 +667GH+7   OT?l&))&1 +667GH+##H-LvQy./ 0dXt$04L*  " " / / ;$++88l**<8L66|DL''lLH'mmBHMM1JM   : m    $ $9 m00??'A'A'C@L %%l3 r#c|jjs|j|||Stj|r_tj |}t |dkDr td|j|d|dtjj|S|j|d|tjj|S)aCalculates embeddings for the given contents. 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 }, ) # Multimodal embeddings are only supported for the Vertex AI API. multimodal_embeddings = client.models.embed_content( model='gemini-embedding-2-exp-11-2025', contents=[ types.Part.from_uri( file_uri='gs://generativeai-downloads/images/scones.jpg', mime_type='image/jpeg', ), ], config={ 'output_dimensionality': 64 }, ) rr HThe embedContent API for this model only supports one content at a time.Nrr)r`rDrbrr rrr rcr relenrrEmbeddingApiTyperrrr`rbrnormalized_contentss r! embed_contentzModels.embed_contentsV    $ $  ux OO((/LL2 !A %    %a("33AA ! "33;; !r#ctj|}tj|}|r@|jr4t j |jrt jdtj|r|j|||S|rd}t|tjr"|jrEt|j}n/t|tr|jdg}|r t|}t||k7r5dj!t#t$|}t&j)d||j|||Stj*|} t&j-d| dg} tj.} d} | dkDr| d z } |j|||} tj0|} | snR| snN| j2r<| j2dj4r#| j2dj4j6sntj8| | }|snt&j-d | d | d z} | dk(rt&j-d | j2dj4}tj:d |}t=j>|}| s| jA|t|tBr"|jE||jE|| jE|| jE|| dkDrtjF|r | | | _$| 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.rrr, 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 and MCP server 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.userrwrv)%r"find_afc_incompatible_tool_indexesparse_config_for_mcp_usagerrhas_mcp_session_usagerUnsupportedFunctionErrorshould_disable_afcr isinstancerGenerateContentConfigrZdictrjoinmapstrloggerwarningget_max_remote_calls_afcinforget_function_maprdrDrvget_function_response_partsContentrcreextendlistappendshould_append_afc_history"automatic_function_calling_history)rr`rbrincompatible_tools_indexes parsed_configoriginal_tools_lengthr indices_strremaining_remote_calls_afcrri function_mapfunc_response_partsfunc_call_contentfunc_response_contents r!generate_contentzModels.generate_contents0D 77?!;;FCM     , ,]-@-@ A  + + B &&}5  # #$" FE77 8 <<"%fll"3  fd # 7B' "%e*  ' (,A AiiC)C DE  D    # #$".!F!F" KK 01K0LAN?A&,,.H A $q (1fa''(h"22=Al   !!$$Q'//$$Q'//55 (DD L!  kk$QCy12 A% #q ( NO"--a088#mm#h'h /*11(; Hd #)*-.(//0AB(//0EFO %q (R ..}=   -1 Or#c#Ktj|}tj|}|r@|jr4t j |jrt jdtj|r|j|||Ed{y|rd}t|tjr"|jrEt|j}n/t|tr|jdg}|r t|}t||k7r5dj!t#t$|}t&j)d||j|||Ed{ytj*|tj,|} t&j/d| d g} d} d} d} | dkDrw| d z } |j|||}tj0|}| d k(r|D]} |stj2|| }|  | j4r<| j4dj6r#| j4dj6j8sntj:| |} | rtj2|| }| n|D]8} tj<|r| | _tj2|| }| :| H| j4r<| j4dj6r#| j4dj6j8sytj:| |} |sy| syt&j/d | d | d z} | dk(rt&j/d | | j4| j4dj6}tj@d| }tCjD|}| s| jG|t|tHr$|"|jK||jK||| jK|| jK|| dkDrvyy77ҭw)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. r`rNrrraz_Tools at indices [%s] are not compatible with automatic function calling. AFC will be disabled.rcrdr rerfrgrhri)&rrjrkrrrlrrmrnrrorrprZrqrrrrsrtrurv'raise_error_for_afc_incompatible_configrwrxryappend_chunk_contentsrdrDrvrzrrr{rcrer|r}r~)rr`rbrrrrrrrrchunkrrrrrrs r!generate_content_streamzModels.generate_content_stream|s2D 77?!;;FCM     , ,]-@-@ A  + + B &&}5../ ! FE77 8 <<"%fll"3  fd # 7B' "%e*  ' (,A AiiC)C DE  -  ../ 88G!-!F!F" KK 01K0LAN?A& E A $q (1fa../h"22=Al a E#99(EJHK$$''*22''*2288"."J"J|# '%;;HeLhk! ( E  3 3M B2  4"77%H(+   M####A&..##A&..44 *FF <    kk$QCy12 A% #q ( NO  u//;!,,Q/77 % %! <<)1 , 3 3H = h %*;*G //+ , /// 0  ( , 3 34E F*112GHU %q (Q*s3BP?P9CP?*P<+D P?9Fs:(()L )-% |<< '881  + +//<< -<,M,M)01++)*KH Or#c,|j||||S)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. rrrr`rrrs r! edit_imagezModels.edit_imageus)b   )  r#chtj|||||xsi}t|tjr|j }n t |}tj |jdd|jdd|jdd|jdd|jdd|jdd|jd d|jd d|jd d|jd d }d|d<d|d<|j||||S)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. rrNrrrrrrrrr rrrrrrrrrrupscalerr r rUpscaleImageParametersroUpscaleImageConfigrrq_UpscaleImageAPIConfigDictrr rr`rrr config_dct api_configs r! upscale_imagezModels.upscale_images8B   % \rF&%223$$&j>*>E#(:DA#->> ($$ 'NN+@$G",.. '# ~~h-J$#Jv%&J!"   %  r#c D|s|s|r |r tdi}|jjs|rt|tj r|j }n t|}|jdr|jdrt j |jd|jd}nL|jjs5|r2t|tjr#|j }|jdi}nht|}t|jdtj r4|jdt j } | j }|r|jdrz|jdrit j|jd|jdt j |jd|jd }|j|||||| S) Generates videos based on an input (text, image, or video) and configuration. The following use cases are supported: 1. Text to video generation. 2a. Image to video generation (additional text prompt is optional). 2b. Image to video generation with frame interpolation (specify last_frame in config). 3. Video extension (additional text prompt is optional) Args: model: The model to use. prompt: The text prompt for generating the videos. Optional for image to video and video extension use cases. This argument is deprecated, please use source instead. image: The input image for generating the videos. Optional if prompt is provided. This argument is deprecated, please use source instead. video: The input video for video extension use cases. Optional if prompt or image is provided. This argument is deprecated, please use source instead. source: The input source for generating the videos (prompt, image, and/or video) config: Configuration for generation. Usage: ``` operation = client.models.generate_videos( model="veo-2.0-generate-001", source=types.GenerateVideosSource( prompt="A neon hologram of a cat driving at top speed", ), ) while not operation.done: time.sleep(10) operation = client.operations.get(operation) operation.result.generated_videos[0].video.uri ``` MSource and prompt/image/video are mutually exclusive. Please only use source.rrr@rr@rrrrrrrQ rrrrorVideorrqrGenerateVideosSourcerV rr`rrrrr video_dct source_dct video_objs r!generate_videoszModels.generate_videossb %5f  '  !#I    $ $ E5;; '$$& K u )-- ">  e$ k0J    & &6 FE66 7&&( NN7B/ &\ jnnW-u{{ ;#->>'5;;=#I)**,) y}}U+ m0L++>>(+..)++MM%(#-- 4    ! r#c|tj|jxstj}|jd|_|j j r6|j}|js|j}|r|dznd|_td|j|j||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' ... rT&filter=labels.tune-type:*labels.tune-type:*r) rr6rListModelsConfigrrrcopyrrr:rr filter_values r!r}z Models.listTs8 ##6299 $  ! ! #  f   {{}f   }}  7 7%    & !  r#)G__name__ __module__ __qualname__rtrrContentListUnionContentListUnionDictrGenerateContentConfigOrDictrrrr ContentUnionContentUnionDictr[EmbedContentConfigOrDictr r GenerateImagesConfigOrDictrrr}_ReferenceImageAPIOrDictEditImageConfigOrDictrr ImageOrDict_UpscaleImageAPIConfigOrDictrr RecontextImageSourceOrDictRecontextImageConfigOrDictr%r'SegmentImageSourceOrDictSegmentImageConfigOrDictr*r+GetModelConfigOrDictr1rListModelsConfigOrDictr7r:UpdateModelConfigOrDictr?DeleteModelConfigOrDictrDrBCountTokensConfigOrDictrHrIComputeTokensConfigOrDictrMrN VideoOrDictGenerateVideosSourceOrDictGenerateVideosConfigOrDictrTrVr^rrrrUpscaleImageConfigOrDictrrrr#r!rrs!=A OOe,,e.H.HHI O u889 O  $$ Ol=A TTe,,e.H.HHI T u889 T --. Tx  =A9=hh &&(B(BB C h ""E$:$:: ;h#5#9#9:hu556h !!h^<@ JJ J u778 J  ## Jd7; @@ @ U;;< @ u223 @ @P>B @@   @  @ u99: @ !!@N<@ jj.. j u778 j  ## jb:> SS,, S u556 S  !! SlKO;;#+E,F,F#G; {{;|AEE < <=E EV9= @@u445 @  {{ @L9= FFu445 F    FZ9= ]]e,,e.H.HHI ] u445 ]    ]H;? SSe,,e.H.HHI S u667 S  "" Sr#+/+/;?;?NNsm N e'' ( N e'' ( Nu778Nu778N $$Nj:> CCe,,e.H.HHI C u556 C  !! CT=A __** _ u889 _  $$ _L=A @I@I** @I u889 @I --. @IN<@ 55 5 u778 5  ## 5z7; 66 6 U;;< 6 u223 6 6|:> HH   H  H u556 H !!H\#+/+/;?;?[[sm [ e'' ( [ e'' ( [u778[u778[ $$[@8<0u3340 U[[ 0r#rc6 eZdZdddedeej ejfdeejdejfdZ dddedeej ejfdeejde e ejfdZddddd dedeeej ejfd eeejej fd eej"deej$dej&f d Zddded edeej*dej,fdZddded edeej2deej4dej6f dZdddedej:dedeej<dej>f dZ dddedejBdeejDdejFfdZ$dddedejJdeejLdejNfdZ(dddedeejRdejTfdZ+dddeejXdejZfdZ.dddedeej^dejTfdZ0dddedeejbdejdfdZ3dddedeej ejfdeejhdejjfdZ6dddedeej ejfdeejndejpfdZ9ddddddded eedeej:deejtdeejvdeejxdejzfdZ>dddedeej ejfdeejdejfd Z?dddedeej ejfdeejde ejfd!Z@ddded edeej2deej4dej6f d"ZAdddeejXdeBejTfd#Zddded edeej*dej,fd$ZCdddedej:dedeejdej>f d%ZEddddddded eedeej:deejtdeejvdeejxdejzfd&ZFdddedeej ejfdeej$dej&fd'ZGy)( AsyncModelsNrr`rbrrc4Ktj|||}|jjr@t |j|d|}|j d}|rdj |}nBd}n?t|j|d|}|j d}|rdj |}nd}|j d}|r|dt|}|jddd} |j,|jj|jj} tj|}tj|}|jjd||| d{} |@t!|ddr3tj"| } |jj%| | S| j&sint)j*| j&} |jjr t-| d|} |jjs t/| d|} tj"j1| |j3 } tj4| j6 | _|jj%| | S7+wr)rrrrrbrrr`r rrrr rr async_requestrrrrrrrorkrrrrrjrs r!rzAsyncModels._generate_contentsw66O   9   OT?l&))&1 (334DE(8   OT?l&))&1 (334DE(##H-LvQy./ 0dXt$04L*  " " / / ;$++88l**<8L66|DL%%33lLHg-t22XNl '' 5 &mmBHMM1JM   : m    $ $9 m00??'A'A'C@L&+%7%7  &L" %%l3 =sE(J*J+D+Jc< Ktj||| jjr@t j d }|j d}|rdj |}nBd}n?tj d }|j d}|rdj |}nd}|j d}|r|dt|}|jddd} j, jj jj}tj|}tj|}|t|ddr t!djj#d |||d{ fd } | S7w) NrrarrrrrrrcK23d{}|jsintj|j}jjr t |d}jjs t |d}tjj|j}tj|j|_ jj||76yw)Nrr)rrrrrrorkrrrrrrrjr)rrrrresponse_streamrs r!async_generatorz=AsyncModels._generate_content_stream..async_generators+"*--TZZ 5N    $ $>T? -((=T? -44CC"?+E+E+GD */););$$* & )),7+Os&C;C9C7C9C)C;7C99C;)rrrrrbrrr`r rrrr rrrrasync_request_streamed) rr`rbrrrrrrrrrs ` @@r!rz$AsyncModels._generate_content_streams66O   9   OT?l&))&1 6AA  78   OT?l&))&1 6AA  7##H-LvQy./ 0dXt$04L*  " " / / ;$++88l**<8L66|DL g-t    !,,CClLO0  9sFFFFrrDrcKtj|||||}|jjrct |j|d|}|j d}t j|jrdnd} |r| j|} nB| } n?t|j|d|}|j d}|rdj|} nd} |j d} | r| dt| } |jd dd} |j,|jj|jj} tj |}tj"|}|jj%d | || d{} | j&sint)j*| j&}|jjr t-|d|}|jjs t/|d|}tj0j3||j5 }tj6| j8 |_|jj=||S7w) 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 }, ) rNrarrrrrrrrr)rrrrrrrcr r`rrr rrrr rrrrrrrrr rrrrrjrr s r!r zAsyncModels._embed_content5sYH::- O   =   OT?l&))&1/0/P/PQ`QfQf/g+m~l &&'78<   OT?l&))&1 +667GH+##H-LvQy./ 0dXt$04L*  " " / / ;$++88l**<8L66|DL%%33lLH'mmBHMM1JM   7 m    $ $6 m--<<'A'A'C=L&+%7%7  &L" %%l3 /sF I:I8C)I:rcKtj|||}|jjr@t |j|d|}|j d}|rdj |}nBd}n?t|j|d|}|j d}|rdj |}nd}|j d}|r|dt|}|jddd} |j,|jj|jj} tj|}tj|}|jjd||| d{} | j sint#j$| j } |jjr t'| d|} |jjs t)| d|} tj*j-| |j/ } tj0| j2 | _|jj7| | S7w) z4Private method for generating images asynchronously.rNrarrrrrrr)rrrrr~rrr|r rrrr rrrrrrrrrrrrrrjrrs r!rzAsyncModels._generate_imagess555O   8   OT?l&))&1  ++,<= 7   OT?l&))&1  ++,<= ##H-LvQy./ 0dXt$04L*  " " / / ;$++88l**<8L66|DL%%33lLH'mmBHMM1JM   9 m    $ $8 m//>>'A'A'C?L&+%7%7  &L" %%l3 /E(I*I+C)IrcKtj||||}|jjs t dt |j|d|}|j d}|rdj|}nd}|j d} | r|dt| }|jddd} |j,|jj|jj} tj|}tj|}|jjd ||| d{} | j sint#j$| j } |jjr t'| d|} tj(j+| |j- } tj.| j0 | _|jj5| | S7ŭw) z3Private method for editing an image asynchronously.rrNrarrrrrrr)rrrrrrrrr rrrr rrrrrrrrrrrrrjrrs r!rzAsyncModels._edit_images00) O    $ $ O PP3   OT?l&))&1  ++,<= ##H-LvQy./ 0dXt$04L*  " " / / ;$++88l**<8L66|DL%%33lLH'mmBHMM1JM   4 m**99'A'A'C:L&+%7%7  &L" %%l3 %D4G>6G<7CG>rrcKtj||||}|jjs t dt |j|d|}|j d}|rdj|}nd}|j d} | r|dt| }|jddd} |j,|jj|jj} tj|}tj|}|jjd ||| d{} | j sint#j$| j } |jjr t'| d|} tj(j+| |j- } tj.| j0 | _|jj5| | S7ŭw) z5Private method for upscaling an image asynchronously.rrNrarrrrrrr)rrrrrrrrr rrrr rrrrrrrrrrrrrjrrs r!r zAsyncModels._upscale_image-s66% O    $ $ O PP9   OT?l&))&1  ++,<= ##H-LvQy./ 0dXt$04L*  " " / / ;$++88l**<8L66|DL%%33lLH'mmBHMM1JM   7 m--<<'A'A'C=L&+%7%7  &L" %%l3 %rrcKtj|||}|jjs t dt |j|d|}|j d}|rdj|}nd}|j d}|r|dt|}|jddd} |j,|jj|jj} tj|}tj|}|jjd ||| d{} | j sint#j$| j } |jjr t'| d|} tj(j+| |j- } |jj/| | S7wr")rr$rrrrUrrr rrrr rrrrrrrWr%rrrr&s r!r'zAsyncModels.recontext_imageosn55O    $ $ O PP8   OT?l&))&1  ++,<= ##H-LvQy./ 0dXt$04L*  " " / / ;$++88l**<8L66|DL%%33lLH'mmBHMM1JM   9 m//>>'A'A'C?L %%l3 !D3G5G6B!GcKtj|||}|jjs t dt |j|d|}|j d}|rdj|}nd}|j d}|r|dt|}|jddd} |j,|jj|jj} tj|}tj|}|jjd ||| d{} | j sint#j$| j } |jjr t'| d|} tj(j+| |j- } |jj/| | S7w) 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 ``` r#rNrarrrrrr)rr)rrrrrrr rrrr rrrrrrrr*rrrr&s r!r+zAsyncModels.segment_imagesF33O    $ $ O PP6   OT?l&))&1  ++,<= ##H-LvQy./ 0dXt$04L*  " " / / ;$++88l**<8L66|DL%%33lLH'mmBHMM1JM   7 m--<<'A'A'C=L %%l3 !rcbKtj||}|jjr@t |j|d|}|j d}|rdj |}nBd}n?t|j|d|}|j d}|rdj |}nd}|j d}|r|dt|}|jddd}|j,|jj|jj}tj|}tj|}|jjd|||d{} | j sint#j$| j } |jjr t'| d|} |jjs t)| d|} tj*j-| |j/} |jj1| | S7íwr-)rr0rrrrrrr rrrr rrrrrrrrr1rrrr2s r!rzAsyncModels.get3s//O   2   OT?l&))&1 ""#341   OT?l&))&1 ""#34##H-LvQy./ 0dXt$04L*  " " / / ;$++88l**<8L66|DL%%33 t\<H'mmBHMM1JM   (oNm    $ $' t_Mm;;--'A'A'C.L %%l3 #E'H/)H-*CH/c(Ktj|}|jjr@t |j|d|}|j d}|rdj |}nBd}n?t|j|d|}|j d}|rdj |}nd}|j d}|r=|j dr,|jd}|d|}|r$|dt|z }n|r|dt|}|jd dd}|j,|jj|jj}tj|}tj|}|jjd |||d{} | j sint#j$| j } |jjr t'| d|} |jjs t)| d|} tj*j-| |j/ } tj0| j2 | _|jj7| | S7wr4)rr6rrrrrr rr rrr rrrrrrrrr7rrrrrjrr8s r!r:zAsyncModels._listrs|11O    4   OT?l&))&1 (()9:3   OT?l&))&1 (()9:##H-L ((2'++H5vX01 2d  !Il+,-- vQy./ 0dXt$04L*  " " / / ;$++88l**<8L66|DL%%33 t\<H'mmBHMM1JM   5 m    $ $4 m++::'A'A'C;L&+%7%7  &L" %%l3 /sF%J'J(C)JcbKtj||}|jjr@t |j|d|}|j d}|rdj |}nBd}n?t|j|d|}|j d}|rdj |}nd}|j d}|r|dt|}|jddd}|j,|jj|jj}tj|}tj|}|jjd|||d{} | j sint#j$| j } |jjr t'| d|} |jjs t)| d|} tj*j-| |j/ } |jj1| | S7íwr<)rr>rrrrrrr rrrr rrrrrrrrr1rrrr2s r!r?zAsyncModels.updates 22O   5   OT?l&))&1 ##$454   OT?l&))&1 ""#34##H-LvQy./ 0dXt$04L*  " " / / ;$++88l**<8L66|DL%%33|\H'mmBHMM1JM   (oNm    $ $' t_Mm;;--'A'A'C.L %%l3 #rcKtj||}|jjr@t |j|d|}|j d}|rdj |}nBd}n?t|j|d|}|j d}|rdj |}nd}|j d}|r|dt|}|jddd}|j,|jj|jj}tj|}tj|}|jjd|||d{} | j sint#j$| j } |jjr t'| d|} |jjs t)| d|} tj*j-| |j/} tj0| j2 | _|jj7| | S7wrA)rrCrrrrrrr rrrr rrrrrrrrrDrrrrrjrr2s r!rBzAsyncModels.deletes2 22O   5   OT?l&))&1 ""#344   OT?l&))&1 ""#34##H-LvQy./ 0dXt$04L*  " " / / ;$++88l**<8L66|DL%%33$ lH'mmBHMM1JM   6 m    $ $5 m,,;;'A'A'C<L&+%7%7  &L" %%l3 /sE'I)I*C)IcKtj|||}|jjr@t |j|d|}|j d}|rdj |}nBd}n?t|j|d|}|j d}|rdj |}nd}|j d}|r|dt|}|jddd} |j,|jj|jj} tj|}tj|}|jjd||| d{} | j sint#j$| j } |jjr t'| d|} |jjs t)| d|} tj*j-| |j/ } tj0| j2 | _|jj7| | S7w) 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 rNrarFrrrrrr)rrGrrrrrrr rrrr rrrrrrrrrHrrrrrjrrs r!rIzAsyncModels.count_tokensEs5822O   5   OT?l&))&1 $//0@A$4   OT?l&))&1 $//0@A$##H-LvQy./ 0dXt$04L*  " " / / ;$++88l**<8L66|DL%%33lLH'mmBHMM1JM   6 m    $ $5 m,,;;'A'A'C<L&+%7%7  &L" %%l3 /rcKtj|||}|jjs t dt |j|d|}|j d}|rdj|}nd}|j d}|r|dt|}|jddd} |j,|jj|jj} tj|}tj|}|jjd ||| d{} | j sint#j$| j } |jjr t'| d|} tj(j+| |j- } tj.| j0 | _|jj5| | S7ŭw) 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'?'])] rrNrarKrrrrrr)rrLrrrrgrrr rrrr rrrrrrrmrMrrrrrjrrs r!rNzAsyncModels.compute_tokenss:44O    $ $ O PP7   OT?l&))&1 &112BC&##H-LvQy./ 0dXt$04L*  " " / / ;$++88l**<8L66|DL%%33lLH'mmBHMM1JM   8 m..=='A'A'C>L&+%7%7  &L" %%l3 %sD3G=5G;6CG=rOrcjKtj||||||}|jjr@t |j|d|}|j d} | rdj | } nBd} n?t|j|d|}|j d} | rdj | } nd} |j d} | r| dt| } |jddd} |j,|jj|jj} tj|}tj|}|jjd| || d{} | j sint#j$| j }|jjr t'|d|}|jjs t)|d|}tj*j-||j/ }|jj1||S7íw) z4Private method for generating videos asynchronously.rQNrarRrrrrr)rrSrrrrrrr rrrr rrrrrrrrrTrrrrUs r!rVzAsyncModels._generate_videoss!55 O   8   OT?l&))&1 +667GH+7   OT?l&))&1 +667GH+##H-LvQy./ 0dXt$04L*  " " / / ;$++88l**<8L66|DL%%33lLH'mmBHMM1JM   : m    $ $9 m00??'A'A'C@L %%l3 +sE+H3-H1.CH3cRKtj|}tj|d{\}}tj|r|j |||d{S|rd}t |t jr"|jrEt|j}n/t |tr|jdg}|r t|}t||k7r5djtt|} tj!d| |j |||d{Stj"|} tj%d| dg} t j&} | dkDrx|j |||d{} | d z} | dk(rtj%d tj(||d } | sn!| sn| j*r<| j*dj,r#| j*dj,j.sntj0| | d{}|sn| j*dj,}t j2d |}t5j6|}| s| j9|t |t:r"|j=||j=|| j=|| j=|| dkDrxtj>|r | | | _ | S77777w)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. Nrrrrarbrcrdr rgTis_caller_method_asyncrhri)!rrjparse_config_for_mcp_sessionsrnrrorrprrZrqrrrrsrtrurvrwrxrryrdrDrv!get_function_response_parts_asyncr{rcrer|r}r~rr)rr`rbrrrmcp_to_genai_tool_adaptersrrrrrrrrrrs r!rzAsyncModels.generate_contentHs$P 77?88@@.M-&&}5))*" FE77 8 <<"%fll"3  fd # 7B' "%e*  ' (,A AiiC)C DE  D   ))*".!F!F" KK 01K0LAN?A&,,.H $q (--.h!A% #q ( NO!22 3Dl  !!$$Q'//$$Q'//55 >>    ! "--a088#mm#h'h /*11(; Hd #)*-.(//0AB(//0EFQ %q (V ..}=   -1 Oi A** sa.L'L3L'$L%C L'2L3A%L'L"B%L'>L%?B8L'9!L'L'L'"L'%L'c Ktj|}tj|d{\} tj|r+j |||d{ fd}||||S|rd}t |t jr"|jrEt|j}n/t |tr|jdg}|r t|}t||k7r5djtt|} tj!d| j |||d{ fd}||||Stj"| fd } | |||S7J77>w) 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. Nrc6K23d{}|7 6ywNrr`rbrrrs r!base_async_generatorzAAsyncModels.generate_content_stream..base_async_generator##  %+ 8rrrarbc6K23d{}|7 6ywrrrs r!rzAAsyncModels.generate_content_stream..base_async_generator3rrcKtj|}tjd|dg}d}d}d}|dkDr5|dz } j |||d{}|dkDrtjd|d|dz}|dkDr|dk(rtjdtj | d } |dk(r|23d{}| stj ||}|'|jr<|jdjr#|jdjjsntj|| d{}|rtj ||}||23d{}tj|r||_ tj ||}|A| sy|sy||jdj} tjd | } tj |}|s|j#|t%|t&r$| "|j)| |j)| | |j)| |j)| |dkDr4yy777067 6|H|jr<|jdjr#|jdjjsytj|| d{7}>w) Nrcrdrr rrerfrgTrrhri)rrwrurxrryrrdrDrvrrrrr{rcrer|ror}r~)r`rbrrrrrrrrrrrrs r!rz.async_generator=s #/#H#H#P  kk 23M2Na PAC( e a & * Q66(67   q5 ++(95 6"a'" q5/14 ++H $44 .t  6&  E%;;HeLhk&&))!,44))!,44::$FF\" )'==hN &  E55f=46$99(EJHK "  = !,,Q/77 % %! <<)1 , 3 3H = h %*;*G //+ , /// 0  ( , 3 34E F*112GHm ' * ( &, Xm%%%%a(00%%a(0066  BB sAK1I6A%K1I?I9I? BK1 I< K1K12J6J7J:C8K14K19I?<K1?K1JA%K1)K,*K1)rrjrrnrrorrprrZrqrrrrsrtrurvr) rr`rbrrrrrrrrrrs ` @@r!rz#AsyncModels.generate_content_streamsF 77? 88@@.M-&&}5445h"%= AA! FE77 8 <<"%fll"3  fd # 7B' "%e*  ' (,A AiiC)C DE  D   445h"%= AA88G_IB 5(M ::] A8s4.FE=3F'F(CFF9FFFcHK|j||||d{S7w)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. rNrrs r!rzAsyncModels.edit_images7b!!) "  s " "cKtj|jxstj}|jd|_|j j r6|j}|js|j}|r|dznd|_td|j|j|d{|S7 w)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' ... rNTrrr) rr6rrrrrrrrr:rs r!r}zAsyncModels.lists< ##6299 $  ! ! #  f   {{}f   }}  7 7%   jjj''   (sBC ? C cHK|j|||d{}d}g}|r |js|S|jD]E}|jr&|jjdk(r |j}5|j |Gt j ||}|S7w)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. rNrrrrs r!rzAsyncModels.generate_images s:../L )-% |<< '881  + +//<< -<,M,M)01++)*KH O1sB"B BB"cKtj|||||xsi}t|tjr|j }n t |}tj |jdd|jdd|jdd|jdd|jdd|jdd|jd d|jd d|jd d|jd d }d|d<d|d<|j||||d{S7w)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. rrNrrrrrrrrrrrrr rrrs r!rzAsyncModels.upscale_imageCsFB   % \rF&%223$$&j>*>E#(:DA#->> ($$ 'NN+@$G",.. '# ~~h-J$#Jv%&J!"$$% %  sD7E9D>:Ec `K|s|s|r |r tdi}|jjs|rt|tj r|j }n t|}|jdr|jdrt j |jd|jd}nL|jjs5|r2t|tjr#|j }|jdi}nht|}t|jdtj r4|jdt j } | j }|r|jdrz|jdrit j|jd|jdt j |jd|jd }|j|||||| d {S7w) rrrrr@rrrrrrQNrrs r!rzAsyncModels.generate_videossb %5f  '  !#I    $ $ E5;; '$$& K u )-- ">  e$ k0J    & &6 FE66 7&&( NN7B/ &\ jnnW-u{{ ;#->>'5;;=#I)**,) y}}U+ m0L++>>(+..)++MM%(#-- 4 && '  sH%H.'H,(H.cK|jjs|j|||d{Stj|rgtj |}t |dkDr td|j|||dtjj|d{S|j||tjj|d{S77;7w)aCalculates embeddings for the given contents. Args: model (str): The model to use. contents (list[Content]): The contents to embed. config (EmbedContentConfig): Optional configuration for embeddings. Usage: .. code-block:: python embeddings = await client.aio.models.embed_content( model= 'text-embedding-004', contents=[ 'What is your name?', 'What is your favorite color?', ], config={ 'output_dimensionality': 64 }, ) # Multimodal embeddings are only supported for the Vertex AI API. multimodal_embeddings = await client.aio.models.embed_content( model='gemini-embedding-2-exp-11-2025', contents=[ types.Part.from_uri( file_uri='gs://generativeai-downloads/images/scones.jpg', mime_type='image/jpeg', ), ], config={ 'output_dimensionality': 64 }, ) rNr rXrr)r`rbrrrYr\s r!r^zAsyncModels.embed_contentsV    $ $&&' ((/LL2 !A %   &&%a("33AA '&&"33;; '%s4.C,C&A;C,,C(-4C,!C*"C,(C,*C,)Hrrrrtrrrrrrrrrrrrrr[rr r rrrr}rrrrrrrr rrr%r'rrr*r+rr1rrr7r:rr?rrDrBrrHrIrrMrNrrrrTrVrrrrrrrrr^rr#r!rrsA=A OOe,,e.H.HHI O u889 O  $$ Ol=A YYe,,e.H.HHI Y u889 Y u<<=> YB  =A9=hh &&(B(BB C h ""E$:$:: ;h#5#9#9:hu556h !!h^<@ JJ J u778 J  ## Jd7; @@ @ U;;< @ u223 @ @P>B @@   @  @ u99: @ !!@N<@ jj.. j u778 j  ## jb:> VV,, V u556 V  !! 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