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Nvideosrcr2r-)rQr3r6r-r.r7r8z7_GenerateVideosResponse_from_vertex..rrrrr:r;r-r6r.#_GenerateVideosResponse_from_vertexr rScCsi}t|dgdurt|dgt|dgt|dgdur(t|dgt|dgt|dgdur;t|dgt|dgt|dgdurNt|dgt|dgt|dgdurat|dgt|dgt|dgdurxt|dgt|t|dg||S)Nr^rr!r"r]r$)r(r*rSr+r-r-r.$_GenerateVideosOperation_from_vertexs(rTc @seZdZdddedeejejfdeej dej fddZ dddedeejejfdeej de ej fd d Z dddedeejejfdeejdejfd d Zddded edeejdejfddZddded edeejdeejdejf ddZdddedejdedeejdejf ddZdddedeejdejfddZdddeej dej!fddZ"dddedeej#dejfddZ$dddedeej%dej&fddZ'dddedeejejfdeej(dej)fdd Z*dddedeejejfdeej+dej,fd!d"Z-e./d#dddd$ded eedeejdeej0dej1f d%d&Z2dddedeejejfdeej dej fd'd(Z3dddedeejejfdeej de ej fd)d*Z4ddded edeejdejfd+d,Z5ddded edeejdeejdejf d-d.Z6dddedejdedeej7dejf d/d0Z8dddeej de9ejfd1d2ZdS)3ModelsNrrrrrc 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||| } |jjr|t|j| } nt|j| } tjj| |d} |j| | S Nrrrrz{model}:generateContentr?rpostr]kwargs)r_GenerateContentParameters _api_clientvertexairZget format_maprrpopr http_optionsr convert_to_dictencode_unserializable_typesrequestr.rGenerateContentResponse_from_response model_dump_verify_response selfrrrparameter_model request_dictrequest_url_dictpath query_paramsrc response_dict return_valuer-r-r._generate_contentsZ           zModels._generate_contentc cs6tj|||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||| D]&} |jjrt|j| } nt|j| } tjj| |d} |j| | VqrdS) NrXr%{model}:streamGenerateContent?alt=sserrYrrZr[)rr]r^r_rZr`rarrrbrrcr rdrerequest_streamedr.rrgrhrirjrkr-r-r._generate_content_stream_sf         zModels._generate_content_streamc 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 ||| } |jjr|t|j| } nt|j| } tjj| |d } |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 }, ) rXr{model}:predict{model}:batchEmbedContentsrrYrNrZr[)r_EmbedContentParametersr^r_r^r`rarrrbrrcr rdrerfr6rEmbedContentResponserhrirjrkr-r-r. embed_contentsZ           zModels.embed_contentrc C(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||| } |jjr|t|j| } nt|j| } tjj| |d } |j| | S zGenerates 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. rrrrrxrrYrNrZr[)r_GenerateImagesParametersr^r_rdr`rarrrbrrcr rdrerfr<rGenerateImagesResponserhrirj rlrrrrmrnrorprqrcrrrsr-r-r._generate_imagessZ           zModels._generate_imagesrc Ctj||||d}|jjstdt|j|}|d}|r$d|}nd}|d} | r6|dt| }| ddd} |j durM|j j durM|j j } t |}t |}|jd ||| } |jjrjt|j| } tjj| |d } |j| | S) LEdits 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. rrrr6This method is only supported in the Vertex AI client.rrxrrYrNrZr[)r_EditImageParametersr^r_r)rr`rarrbrrcr rdrerfr>EditImageResponserhrirj rlrrrrrmrnrorprqrcrrrsr-r-r. _edit_imageQsJ2         zModels._edit_imager<rc Cr aUpscales an image. Args: model (str): The model to use. image (Image): The input image for upscaling. upscale_factor (str): The factor to upscale the image (x2 or x4). config (_UpscaleImageAPIConfig): Configuration for upscaling. rr<rrrrrxrrYrNrZr[)r_UpscaleImageAPIParametersr^r_r)rr`rarrbrrcr rdrerfr@UpscaleImageResponserhrirj rlrr<rrrmrnrorprqrcrrrsr-r-r._upscale_imagesJ         zModels._upscale_imagec C&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|||} |jjr{t|j| } nt|j| } tjj| |d} |j| | S Nrrr{name}rrYrr`r[)r_GetModelParametersr^r_rr`rar rrbrrcr rdrerfrIrModelrhrirj rlrrrmrnrorprqrcrrrsr-r-r.r`sP            z Models.getc Cs$tj|d}|jjr t|j|}|d}|rd|}nd}nt|j|}|d}|r3d|}nd}|d}|rE|dt|}| ddd}|j dur\|j j dur\|j j }t |}t |}|jd|||}|jjrzt|j|}nt|j|}tjj||d} |j| | S NrVrz {models_url}rrYrr`r[)r_ListModelsParametersr^r_rr`rarrrbrrcr rdrerfrJrListModelsResponserhrirj rlrrmrnrorprqrcrrrsr-r-r._list6sV           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|||} |jjr{t|j| } nt|j| } tjj| |d } |j| | S Nrrz{model}rrrYrpatchr[)r_UpdateModelParametersr^r_rr`rar&rrbrrcr rdrerfrIrrrhrirjrr-r-r.updateusP            z Models.updatec Cr NrrrrrYrdeleter[)r_DeleteModelParametersr^r_rr`rar(rrbrrcr rdrerfrKr DeleteModelResponserhrirjrr-r-r.rsX           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 rXr{model}:countTokensrrYrNrZr[)r_CountTokensParametersr^r_rr`rar,rrbrrcr rdrerfrLrCountTokensResponserhrirjrkr-r-r. count_tokenssZ           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 ||| } |jjrit|j| } tjj| |d } |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'?'])] rXrr{model}:computeTokensrrYrNrZr[)r_ComputeTokensParametersr^r_r)rr`rarrbrrcr rdrerfrOComputeTokensResponserhrirjrkr-r-r.compute_tokensQsH         zModels.compute_tokens>This method is experimental and may change in future versions.rr<rc Cs*tj||||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||| } |jjr}t|j| } nt|j| } tjj| |d } |j| | S aSGenerates videos based on a text description and configuration. Args: model: The model to use. instances: A list of prompts, images and videos to generate videos from. config: Configuration for generation. Usage: ``` operation = client.models.generate_videos( model="veo-2.0-generate-001", 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 ``` )rrr<rrz{model}:predictLongRunningrrYrNrZr[)r_GenerateVideosParametersr^r_rr`rar=rrbrrcr rdrerfrTr%GenerateVideosOperationrhrirj rlrrr<rrmrnrorprqrcrrrsr-r-r.generate_videoss\!            zModels.generate_videosc Csht|r |j|||dSt|}td|dg}t}d}|dkr|d7}|j|||d}td|d|d8}|dkrItdt|}|sQnU|sTnR|j rd|j dj rd|j dj j senAt ||} | snn8|j dj } tj d | d } t|j|}|s||t|tr|| || || || |dks't|r|d ur||_|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 'publishers/meta/models/llama-3.1-405b-instruct-maas' - `/` separated publisher and model name, for example: 'google/gemini-2.0-flash' or 'meta/llama-3.1-405b-instruct-maas' 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. 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('What is shown in this image?'), types.Part.from_uri('gs://generativeai-downloads/images/scones.jpg', 'image/jpeg') ] ) print(response.text) # The image shows a flat lay arrangement of freshly baked blueberry # scones. rX&AFC is enabled with max remote calls: .rr AFC remote call is done.8Reached max remote calls for automatic function calling.userr9r1Nr should_disable_afcrtget_max_remote_calls_afcloggerinforrgget_function_maprrr1get_function_response_partsContentrrr^extend isinstancelistappendshould_append_afc_history"automatic_function_calling_history) rlrrrremaining_remote_calls_afcrr]i function_mapfunc_response_partsfunc_call_contentfunc_response_contentr-r-r.generate_contentsn @             'zModels.generate_contentc cst|r|j|||dEdHdSt|}td|dg}d}d}d}|dkr|d7}|j|||d} td|d|d8}|dkrOtd t|} |dkr| D]%}| sb|VqZ|jrr|jdjrr|jdjj stn t || }|s|VqZn(| D] }t |r||_ |Vq|jr|jdjr|jdjj sdSt || }| sdS|sdS|jdur|jdj} t jd |d } t|j|}|s||t|tr| dur|| || | dur|| || |dks-dSdS) a?Makes 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 'publishers/meta/models/llama-3.1-405b-instruct-maas' - `/` separated publisher and model name, for example: 'google/gemini-2.0-flash' or 'meta/llama-3.1-405b-instruct-maas' 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. 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. rXNrrrr rrrrr)r rrwrrrrrrr1rrrrrrrr^rrrr) rlrrrrrchunkrrr]rrrr-r-r.generate_content_streamys @                 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 Promptrrrrrrrrr rlrrr api_responserrgenerated_imager]r-r-r.generate_imagess,   zModels.generate_imagescCs|j||||dS)rrrrlrrrrr-r-r. edit_imageFs 1zModels.edit_imagecCstj||||d|p i}t|tjr|}nt|}tj|dd|dd|dd|ddd}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. rrcNrrrrcrrrupscalergr r rUpscaleImageParametersrUpscaleImageConfigridict_UpscaleImageAPIConfigDictr`rrlrr<rr config_dct api_configr-r-r. upscale_image~s4!      zModels.upscale_imagecCsltj|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' ... rVNT&filter=labels.tune-type:*labels.tune-type:*r) rrrListModelsConfigrr^r_copyrrrrlr filter_valuer-r-r.rs&   z Models.list):__name__ __module__ __qualname__strrrContentListUnionContentListUnionDictrGenerateContentConfigOrDictrgrtrrwEmbedContentConfigOrDictr{r|GenerateImagesConfigOrDictrrr_ReferenceImageAPIOrDictEditImageConfigOrDictrr ImageOrDict_UpscaleImageAPIConfigOrDictrrGetModelConfigOrDictrr`ListModelsConfigOrDictrrUpdateModelConfigOrDictrDeleteModelConfigOrDictrrCountTokensConfigOrDictrrComputeTokensConfigOrDictrrr experimental_warningGenerateVideosConfigOrDictrrrrrrUpscaleImageConfigOrDictrrr-r-r-r.rUs J N a S k E = C C H _ P b }  = > ErUc @seZdZdddedeejejfdeej dej fddZ dddedeejejfdeej de e ej fd d Zdddedeejejfdeejdejfd d Zddded edeejdejfddZddded edeejdeejdejf ddZdddedejdedeejdejf ddZdddedeejdejfddZ dddeej!dej"fddZ#dddedeej$dejfddZ%dddedeej&dej'fddZ(dddedeejejfdeej)dej*fdd Z+dddedeejejfdeej,dej-fd!d"Z.e/0d#dddd$ded eedeejdeej1dej2f d%d&Z3dddedeejejfdeej dej fd'd(Z4dddedeejejfdeej de e ej fd)d*Z5ddded edeejdeejdejf d+d,Z6dddeej!de7ejfd-d.Zddded edeejdejfd/d0Z8dddedejdedeej9dejf d1d2Z:dS)3 AsyncModelsNrVrrrrc s0tj|||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} |jjrt|j| } nt|j| } tjj| |d} |j| | SrW)rr]r^r_rZr`rarrrbrrcr rdre async_requestr.rrgrhrirjrkr-r-r.rts\            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 |}jd|||IdHfdd } | S) NrXrrurrYrrZcsb2z*3dHW}jjrtj|}ntj|}tjj|d}j||Vq6dS)Nr[) r^r_r.rrrgrhrirj)rrrsrmresponse_streamrlr-r.async_generatorss z=AsyncModels._generate_content_stream..async_generator)rr]r^r_rZr`rarrrbrrcr rdreasync_request_streamed) rlrrrrnrorprqrcrr-rr.rw:sP         z$AsyncModels._generate_content_streamc s0tj|||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} |jjrt|j| } nt|j| } tjj| |d } |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 }, ) rXrrxryrrYrNrZr[)rrzr^r_r^r`rarrrbrrcr rdrerr6rr{rhrirjrkr-r-r.r|s\            zAsyncModels.embed_contentrc 0tj|||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} |jjrt|j| } nt|j| } tjj| |d } |j| | Sr~)rrr^r_rdr`rarrrbrrcr rdrerr<rrrhrirjrr-r-r.rs\            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} |jjrnt|j| } tjj| |d } |j| | S) VEdits 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. rrrrxrrYrNrZr[)rrr^r_r)rr`rarrbrrcr rdrerr>rrhrirjrr-r-r.r1sL2          zAsyncModels._edit_imager<rc r r)rrr^r_r)rr`rarrbrrcr rdrerr@rrhrirjrr-r-r.rsL          zAsyncModels._upscale_imagec .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|||IdH} |jjrt|j| } nt|j| } tjj| |d} |j| | Sr)rrr^r_rr`rar rrbrrcr rdrerrIrrrhrirjrr-r-r.r`sR             zAsyncModels.getc s,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|||IdH}|jjr~t|j|}nt|j|}tjj||d} |j| | Sr)rrr^r_rr`rarrrbrrcr rdrerrJrrrhrirjrr-r-r.rsX            zAsyncModels._listc s.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|||IdH} |jjrt|j| } nt|j| } tjj| |d } |j| | Sr)rrr^r_rr`rar&rrbrrcr rdrerrIrrrhrirjrr-r-r.rUsR             zAsyncModels.updatec r r)rrr^r_rr`rar(rrbrrcr rdrerrKr rrhrirjrr-r-r.rsZ            zAsyncModels.deletec r ) 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 rXrrrrYrNrZr[)rrr^r_rr`rar,rrbrrcr rdrerrLrrrhrirjrkr-r-r.rs\            zAsyncModels.count_tokensc stj|||d}|jjstdt|j|}|d}|r$d|}nd}|d}|r6|dt|}| ddd} |j durM|j j durM|j j } t |}t |}|jd ||| IdH} |jjrmt|j| } tjj| |d } |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'?'])] rXrrrrrYrNrZr[)rrr^r_r)rr`rarrbrrcr rdrerrOrrhrirjrkr-r-r.r1sJ          zAsyncModels.compute_tokensrrc s2tj||||d}|jjr$t|j|}|d}|r!d|}nd}nt|j|}|d}|r7d|}nd}|d} | rI|dt| }| ddd} |j dur`|j j dur`|j j } t |}t |}|jd||| IdH} |jjrt|j| } nt|j| } tjj| |d } |j| | Sr)rrr^r_rr`rar=rrbrrcr rdrerrTr%rrhrirjrr-r-r.rs^!             zAsyncModels.generate_videosc sXt|r|j|||dIdHSt|}td|dg}t}|dkr|j|||dIdH}|d8}|dkrAtdt|}|sInU|sLnR|j r\|j dj r\|j dj j s]nAt ||}|sfn8|j dj } tj d|d } t|j|}|s||t|tr|| || || || |dks)t|r|dur||_|S) a.Makes an API request to generate content using a model. Some models support multimodal input and output. Usage: .. code-block:: python from google.genai import types from google import genai client = genai.Client( vertexai=True, project='my-project-id', location='us-central1' ) response = await client.aio.models.generate_content( model='gemini-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. rXNrrrr rrrr) rlrrrrrr]rrrrr-r-r.rsj $              &zAsyncModels.generate_contentcsRt|rj|||dIdHfdd}||||Sfdd}||||S)a_Makes 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 'publishers/meta/models/llama-3.1-405b-instruct-maas' - `/` separated publisher and model name, for example: 'google/gemini-2.0-flash' or 'meta/llama-3.1-405b-instruct-maas' 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. 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 awiat 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. rXNcs 2z 3dHW}|Vq6dSrbr-)rrrr)r]r-r.base_async_generatorszAAsyncModels.generate_content_stream..base_async_generatorc st|}td|dg}d}d}d}|dkr|d7}j|||dIdH}td|d|d8}|dkr@tdt|} |dkrw|2z)3dHW}| sW|VqK|jrg|jdjrg|jdjjsin t || }|st|VqK6n-|2z3dHW}t |r||_ |Vqy6|jr|jdjr|jdjjsdSt || }| sdS|sdS|jdj} t j d |d } tj|}|s||t|tr| dur|| || | dur|| || |dksdSdS) Nrrrr rXrrrrr)r rrrrwrrrr1rrrrrrrr^rrrr) rrrrrrrrr]rrr)rlr-r.rs               z.async_generator)r rrw)rlrrrrrr-)r]rlr.r:s @    Sz#AsyncModels.generate_content_streamcs|j||||dIdHS)r 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' ... rVNTrrr) rrrrrr^r_rrrrrr-r-r.rs(  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.rFs.    zAsyncModels.generate_imagescstj||||d|p i}t|tjr|}nt|}tj|dd|dd|dd|ddd}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. rrcNrrrrrrgr rrrr-r-r.r}s6!       zAsyncModels.upscale_image);rrrrrrrrrrrgrtrrrwrr{r|rrrrrrrrrrrrrrr`rrrrrrrrrrrrrrr rrrrrrrrrrrr-r-r-r.rs J  S a S k E = C C H _ O b _  ' ; 9 =rrb)loggingtypingrrrrrr urllib.parserr r r r rrr^rrr(rr*pagersrr getLoggerrrobjectr/r<rXr\r`rcrjrpr{rrrrrrrrrrrrr rrr$r&r(r*r,r1r9r=rArCrHrIrKrLrMrNrOrPrQrRrSrTrUrXrZr]r^rbrdrhrqrwrzrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrr rrrrr%r&r'r(r+r.r0r2r5r6r7r8r:r<r>r@rDrErIrJrKrLrOrPrQrSrT BaseModulerUrr-r-r-r.s              /    R             0              )  (  $  (  j      #            (    B  &  /    R             0              '  (  ,  #  r              ?  r  )  ,  *    #          )  (  "  R  #  -      7                  #  !       4             $  &  /      7  %              &  !          .         $ !f