5P@ix4 ddlmZddlZddlmZmZddlmcmZddl m Z ddl m Z ddl mZddlmZddlmZdd lmZdd  dd Zdd dd Zdd  dd Z d d!dZddd d"dZddd d#dZddddddddddddd d$dZej6 d dd  d%dZej6dd  d&dZ d dd  d'dZdZdZ d d(dZy))) annotationsN)AnyLiteral) operations)get_default_model_client) model_types) operation)protobuf_helpers)field_mask_pb2clientctj|}|jdr t||S|jdr t ||St d)aYGiven a model name, fetch the `types.Model` or `types.TunedModel` object. ``` import pprint model = genai.get_tuned_model(model_name): pprint.pprint(model) ``` Args: name: The name of the model to fetch. client: The client to use. Returns: A `types.Model` or `types.TunedModel` object. models/r tunedModels/z7Model names must start with `models/` or `tunedModels/`)rmake_model_name startswithget_base_modelget_tuned_model ValueError)namer s d/home/www/lebenam.kofcorporation.com/venv/lib/python3.12/site-packages/google/generativeai/models.py get_modelrsR$  & &t ,D y!d622  (tF33RSSc| t}tj|}|jdst d||j |}t |j|}tjdi|S)a.Get the `types.Model` for the given base model name. ``` import pprint model = genai.get_model('models/chat-bison-001'): pprint.pprint(model) ``` Args: name: The name of the model to fetch. client: The client to use. Returns: A `types.Model`. rz1Base model names must start with `models/`, got: r) rrrrrrtypeto_dictModelrr results rrr8s{ ~)+  & &t ,D ??9 %LTFSTT   4  (F &\ ! !& )F    &v &&rc| t}tj|}|jds t d|j |}tj |S)aCGet the `types.TunedModel` for the given tuned model name. ``` import pprint model = genai.get_tuned_model('tunedModels/my-model-1234'): pprint.pprint(model) ``` Args: name: The name of the model to fetch. client: The client to use. Returns: A `types.TunedModel`. rz0Tuned model names must start with `tunedModels/`r)rrrrrrdecode_tuned_modelr s rrrTs]$~)+  & &t ,D ??> *KLL  # # # .F  ) )& 11rct|tr0|jdrt||}|j}|S|}|St|t j r|j}|St|t jr|j}|St|tjr|j}|St|tj r't|dd}|s|jj}|Std|)Nrr base_modelzCannot understand model: ) isinstancestrrrr%r TunedModelrrglmgetattrtuned_model_source TypeError)modelr r%s rget_base_model_namer.ss%   N +eF3E))J J  E;11 2%%   E;,, -ZZ   E399 %ZZ   E3>> *UL$7 11<4U ##E*,,U334r5 text_inputoutput) id display_name description temperaturetop_ptop_k epoch_count batch_size learning_rate input_key output_keyr c | t} tj|}t|}|j drd|i}n(|j drd||di}nt d|dtj || | }tj|| | }tj|| }tjd i|||||||d }| jt|| }tjj|S)a Launches a tuning job to create a TunedModel. Since tuning a model can take significant time, this API doesn't wait for the tuning to complete. Instead, it returns a `google.api_core.operation.Operation` object that lets you check on the status of the tuning job, or wait for it to complete, and check the result. After the job completes you can either find the resulting `TunedModel` object in `Operation.result()` or `palm.list_tuned_models` or `palm.get_tuned_model(model_id)`. ``` my_id = "my-tuned-model-id" operation = palm.create_tuned_model( id = my_id, source_model="models/text-bison-001", training_data=[{'text_input': 'example input', 'output': 'example output'},...] ) tuned_model=operation.result() # Wait for tuning to finish palm.generate_text(f"tunedModels/{my_id}", prompt="...") ``` Args: source_model: The name of the model to tune. training_data: The dataset to tune the model on. This must be either: * A `glm.Dataset`, or * An `Iterable` of: *`glm.TuningExample`, * {'text_input': text_input, 'output': output} dicts, or * `(text_input, output)` tuples. * A `Mapping` of `Iterable[str]` - use `input_key` and `output_key` to choose which columns to use as the input/output * A csv file (will be read with `pd.read_csv` and handles as a `Mapping` above). This can be: * A local path as a `str` or `pathlib.Path`. * A url for a csv file. * The url of a Google Sheets file. * A JSON file - Its contents will be handled either as an `Iterable` or `Mapping` above. This can be: * A local path as a `str` or `pathlib.Path`. id: The model identifier, used to refer to the model in the API `tunedModels/{id}`. Must be unique. display_name: A human-readable name for display. description: A description of the tuned model. temperature: The default temperature for the tuned model, see `types.Model` for details. top_p: The default `top_p` for the model, see `types.Model` for details. top_k: The default `top_k` for the model, see `types.Model` for details. epoch_count: The number of tuning epochs to run. An epoch is a pass over the whole dataset. batch_size: The number of examples to use in each training batch. learning_rate: The step size multiplier for the gradient updates. client: Which client to use. Returns: A [`google.api_core.operation.Operation`](https://googleapis.dev/python/google-api-core/latest/operation.html) rr%rr+) tuned_modelr%zNot understood: `source_model=`)rCrD)r@rArB) training_datahyperparameters)r;r<r=r>r? tuning_task)tuned_model_idrFr)rrrr.rrencode_tuning_datar)Hyperparameters TuningTaskr(create_tuned_modeldictrCreateTunedModelOperationfrom_core_operation) source_modelrHr:r;r<r=r>r?r@rArBrCrDr source_model_namebase_model_namerIrJrFr s rrOrOs$P~)+#33LA),7O##I.$&78  % %n 5 0-#  4|oQ7822zM))#O ..#'K .. !K))$bk*Z[I  / / C CI NNrcyNrrFupdatesr s rupdate_tuned_modelrZ: rcyrWrrXs rrZrZDr[rc | t}t|tr|}t|tst dt ||j |}t|}tj}|jD]}|jj||jD]\}}t|||nt|tj rV| t#d|j$}|j |}t'j(|j*|j*}nt dt |d|j-tj.||}t1j2|S)zGPush updates to the tuned model. Only certain attributes are updatable.z]When calling `update_tuned_model(name:str, updates: dict)`, `updates` must be a `dict`. got: rzfWhen calling `update_tuned_model(tuned_model:glm.TunedModel, updates=None)`,`updates` must not be set.zxFor `update_tuned_model(tuned_model:dict|glm.TunedModel)`,`tuned_model` must be a `dict` or a `glm.TunedModel`. Got a: `rG)rF update_mask)rr&r'rPr,rr_flatten_update_pathsr FieldMaskkeyspathsappenditems _apply_updater)r(rrr field_mask_pbrZUpdateTunedModelRequestrr#) rFrYr rrfpathvaluewasr!s rrZrZNs~)++s#'4(W (  ,,$,7 '0#--/ LLN *D    # #D ) *"==? 4KD% +tU 3 4 K 0  -  $$$$/%00+//J  MMQR]M^L__` b   & & ## TF  ) )& 11rci}|jD]F\}}t|tr,t|jD]\}}|||d|<B|||<H|S)N.)rdr&rPr_)rY new_updateskeyrjsub_key sub_values rr_r_~suKmmo% U eT "&;E&B&H&H&J <"2; se1WI./ < %K  % rcr|jd}|ddD]}t||}t||d|y)Nrm)splitr*setattr)thingrirjpartsparts rreres@ JJsOEcr %t$% E59e$rcj| t}tj|}|j|y)Nr)rrrdelete_tuned_model)rFr rs rrzrzs2~)+  & &{ 3D 4(r)rmodel_types.AnyModelNameOptionsreturnz*model_types.Model | model_types.TunedModel)rz model_types.BaseModelNameOptionsr|zmodel_types.Model)r!model_types.TunedModelNameOptionsr|model_types.TunedModelrW)r-r{r glm.ModelServiceClient | None)r0 int | Noner rr|zmodel_types.ModelsIterable)r0rr rr|zmodel_types.TunedModelsIterable)rSr{rHzmodel_types.TuningDataOptionsr: str | Noner;rr<rr= float | Noner>rr?rr@rrArrBrrCr'rDr'r rr|z$operations.CreateTunedModelOperation)rFzglm.TunedModelrYNoner rr|r~)rFr'rYzdict[str, Any]r rr|r~)rFzstr | glm.TunedModelrYzdict[str, Any] | Noner rr|r~)rFr}r rr|r) __future__rtypingrrgoogle.ai.generativelanguageaigenerativelanguager)google.generativeairgoogle.generativeai.clientrgoogle.generativeai.typesrgoogle.api_corer r google.protobufr rrrr.r3r7rOoverloadrZr_rerzrrrrs# ***?1%,*6:T )T/T6FJ':8<2 +22@UY *4Q6,0)) *) )>,044 *4% 4@#" $"!"&!,0rO1rO0rO  rO  rO  rOrO rO rOrOrO rOrOrO *rO *!rOj -1     *      -1     *     &*-2-1 -2%-2 "-2 * -2  -2` %-1)2) )) )r