o ߭jeU @s(ddlmZddlZddlZddlZddlmZmZddlm m Z ddl m Z ddl mZddlmZddlmZddlmZdd lmZdd lmZdd lmZd_ddZd`ddZdaddZdbdd Zdcd"d#Z dddddd$ded,d-Z dddddd$ded.d/Zddddddddd0dfd;d<Zd=Zd=ddddddddddd> dgdDdEZ e!e j"d=ddddddddddd> dhdGdHZ#ej$j%ej$j&fdIkrdJdKiZ'niZ'ej(e!ej)j"ej*diie'dLdMiGdNdOdOej)Z)djdTdUZ+  dkdldVdWZ,  dkdmdXdYZ-ddddedddZdnd]d^Z.dS)o) annotationsN)IterableList)get_default_discuss_client) get_default_discuss_async_client) string_utils)protos) discuss_types) helper_types) model_types)palm_safety_typescontentdiscuss_types.MessageOptionsreturnprotos.MessagecCs0t|tjr|St|trtj|dSt|S)zAsz"_make_messages..cs|] }|jr|jVqdSNauthorrmsgrrr Cz!_make_messages..N0zDInvalid sequence: Authors in the discussion must alternate strictly.csrrr r"rrrr$Mr%1css|]}|jVqdSrr r"rrrr$Ws)rrdictrrrsetlenpopr AuthorErrorall enumerater!)r even_authors even_author odd_authors odd_authorauthorsir#rrr_make_messages-s2     r7itemdiscuss_types.ExampleOptionsprotos.ExamplecCst|tjr|St|tr&|}t|d|d<t|d|d<t|St|tr)rBresultpairnr8r#primerrrr_make_examples_from_flatts,   rMdiscuss_types.ExamplesOptionscCst|tjr |gSt|trt|gSt|}|s|S|d}t|tr9d|vr,t|Sd|vr4d|vs8tdn t|tj rCt|Sg}|D] }| t|qG|S)a Creates a list of `protos.Example` objects from the provided examples. This function takes various types of example content inputs and creates a list of `protos.Example` objects. It handles the conversion of different input types and ensures the appropriate structure for creating valid examples. Args: examples: The examples to convert. Returns: A list of `protos.Example` objects created from the provided examples. rr r;r<zwInvalid dictionary format: To create an `Example` instance, the dictionary must contain both `input` and `output` keys.) rrr>r*rAr@rM TypeErrorr MESSAGE_OPTIONSrH)rBfirstrIr8rrr_make_exampless,     rRcontextrBrprompt"discuss_types.MessagePromptOptionsrT str | None$discuss_types.ExamplesOptions | None$discuss_types.MessagesOptions | Noneprotos.MessagePromptcCs|dur t|||d}n$|dup|dup|du}|rtdt|tjr&|St|tr,nd|i}t|}|tj sGt d|tj d| dd}|durWt ||d<| dd}|durgt ||d<dd |D}|S) a Creates a `protos.MessagePrompt` object from the provided prompt components. This function constructs a `protos.MessagePrompt` object using the provided `context`, `examples`, or `messages`. It ensures the proper structure and handling of the input components. Either pass a `prompt` or it's component `context`, `examples`, `messages`. Args: prompt: The complete prompt components. context: The context for the prompt. examples: The examples for the prompt. messages: The messages for the prompt. Returns: A `protos.MessagePrompt` object created from the provided prompt components. NrSzInvalid configuration: Either `prompt` or its fields `(context, examples, messages)` should be set, but not both simultaneously.rzHInvalid prompt dictionary: Extra entries found that are not recognized: z. Please check the keys.rBcSsi|] \}}|dur||qSrr)rkvrrr sz-_make_message_prompt_dict..)r*rErr MessagePromptr+keysissubsetr MESSAGE_PROMPT_KEYSKeyErrorgetrRr7items)rUrTrBr flat_promptr_rrr_make_message_prompt_dicts:        rfcCst||||d}t|S)zLCreates a `protos.MessagePrompt` object from the provided prompt components.rUrTrBr)rfrr^rgrrr_make_message_prompt s rh)rTrBr temperaturecandidate_counttop_ptop_krUmodel&model_types.AnyModelNameOptions | Noneri float | Nonerj int | Nonerkrl)discuss_types.MessagePromptOptions | Noneprotos.GenerateMessageRequestc Cs0t|}t||||d}tj||||||dS)zICreates a `protos.GenerateMessageRequest` object for generating messages.rg)rmrUrirkrlrj)r make_model_namerhrGenerateMessageRequest rmrTrBrrirjrkrlrUrrr_make_generate_message_requests rvzmodels/chat-bison-001) rmrTrBrrirjrkrlrUclientrequest_optionsrwglm.DiscussServiceClient | Nonerx&helper_types.RequestOptionsType | Nonediscuss_types.ChatResponsec Cs(t|||||||||d } t| | | dS)aS Calls the API to initiate a chat with a model using provided parameters Args: model: Which model to call, as a string or a `types.Model`. context: Text that should be provided to the model first, to ground the response. If not empty, this `context` will be given to the model first before the `examples` and `messages`. This field can be a description of your prompt to the model to help provide context and guide the responses. Examples: * "Translate the phrase from English to French." * "Given a statement, classify the sentiment as happy, sad or neutral." Anything included in this field will take precedence over history in `messages` if the total input size exceeds the model's `Model.input_token_limit`. examples: Examples of what the model should generate. This includes both the user input and the response that the model should emulate. These `examples` are treated identically to conversation messages except that they take precedence over the history in `messages`: If the total input size exceeds the model's `input_token_limit` the input will be truncated. Items will be dropped from `messages` before `examples` messages: A snapshot of the conversation history sorted chronologically. Turns alternate between two authors. If the total input size exceeds the model's `input_token_limit` the input will be truncated: The oldest items will be dropped from `messages`. temperature: Controls the randomness of the output. Must be positive. Typical values are in the range: `[0.0,1.0]`. Higher values produce a more random and varied response. A temperature of zero will be deterministic. candidate_count: The **maximum** number of generated response messages to return. This value must be between `[1, 8]`, inclusive. If unset, this will default to `1`. Note: Only unique candidates are returned. Higher temperatures are more likely to produce unique candidates. Setting `temperature=0.0` will always return 1 candidate regardless of the `candidate_count`. top_k: The API uses combined [nucleus](https://arxiv.org/abs/1904.09751) and top-k sampling. `top_k` sets the maximum number of tokens to sample from on each step. top_p: The API uses combined [nucleus](https://arxiv.org/abs/1904.09751) and top-k sampling. `top_p` configures the nucleus sampling. It sets the maximum cumulative probability of tokens to sample from. For example, if the sorted probabilities are `[0.5, 0.2, 0.1, 0.1, 0.05, 0.05]` a `top_p` of `0.8` will sample as `[0.625, 0.25, 0.125, 0, 0, 0]`. Typical values are in the `[0.9, 1.0]` range. prompt: You may pass a `types.MessagePromptOptions` **instead** of a setting `context`/`examples`/`messages`, but not both. client: If you're not relying on the default client, you pass a `glm.DiscussServiceClient` instead. request_options: Options for the request. Returns: A `types.ChatResponse` containing the model's reply. rurwrequestrx)rv_generate_response rmrTrBrrirjrkrlrUrwrxr}rrrchat8sT r$glm.DiscussServiceAsyncClient | Nonec s0t|||||||||d } t| | | dIdHS)zVCalls the API asynchronously to initiate a chat with a model using provided parametersrur|N)rv_generate_response_asyncrrrr chat_asyncs  r) kw_onlyTinitFc@seZdZUejddddZded<ddZee e j j jdd d Z e jddd Z e e j jj ddddZe e j jjdddZdS) ChatResponsecCsdSrrrrrrszChatResponse.F)defaultreprry_clientcKs"|D] \}}t|||qdSr)rdsetattr)selfkwargskeyvaluerrr__init__szChatResponse.__init__rrWcCs|jdr |jddSdS)Nr )r)rrrrlasts zChatResponse.lastrrcCs$t|}t||}||jd<dS)Nr)rtypeto_dictr)rrrrrrsNrxrzr{cCst|jtjr td|jdurtd|j|}| d| ddt |d|d<|d t |t di|}t||j|dS)Nz~Invalid operation: The 'reply' method cannot be called on an asynchronous client. Please use the 'reply_async' method instead.zInvalid operation: No candidates returned from the model's last response. Please inspect the '.filters' attribute to understand why responses were filtered out. Current filters: candidatesfiltersr)r}rwrxr)rrglmDiscussServiceAsyncClientrOrrErrr-r@rHrrvr~)rrrxr}rrrreplys&   zChatResponse.replycszt|jtjr td|}|d|ddt|d|d<|dt |t di|}t ||jdIdHS)NzqInvalid method call: `reply_async` is not supported on a non-async client. Please use the `reply` method instead.rrr)r}rwr) rrrDiscussServiceClientrOrr-r@rHrrvr)rrr}rrr reply_asyncs  zChatResponse.reply_async)rrW)rrr)rrrxrzrr{)rrrr{)__name__ __module__ __qualname__ dataclassesfieldr__annotations__rpropertyrset_docr rr__doc__setterrrrrrrrs  rr}responseprotos.GenerateMessageResponse;glm.DiscussServiceClient | protos.DiscussServiceAsyncClientcCst||}|d}|d|d<|d|d<|d|d<t||}|dt|d|d<|dr>|dd}nd}|d||dd|d dtd d |i||S) NrUrBrTrrrrrirjrr)rrr-r convert_filters_to_enumsrH setdefaultr)r}rrwrUrrrr_build_chat_responses       rcCs8|duri}|dur t}|j|fi|}t|||Sr)rgenerate_messagerr}rwrxrrrrr~ s  r~cs@|duri}|durt}|j|fi|IdH}t|||Sr)rrrrrrrr0s r)rUrTrBrrmrwrxmodel_types.AnyModelNameOptionsdiscuss_types.TokenCountcCsXt|}t||||d}|duri}|durt}|jd||d|}t||S)zCCalls the API to calculate the number of tokens used in the prompt.rSN)rmrUr)r rsrhrcount_message_tokensrr)rUrTrBrrmrwrxrIrrrr@s r)r rrr)rrrr)r8r9rr:)rBrCrrD)rBrNrrDr) rUrVrTrWrBrXrrYrrZ)rmrnrTrWrBrXrrYrirorjrprkrorlrorUrqrrr)rmrnrTrWrBrXrrYrirorjrprkrorlrorUrqrwryrxrzrr{)rmrnrTrWrBrXrrYrirorjrprkrorlrorUrqrwrrxrzrr{r)r}rrrrrwrrr)NN)r}rrrwryrxrzrr)r}rrrwrrxrzrr)rUrVrTrWrBrXrrYrmrrwrrxrzrr)/ __future__rrsysrFtypingrrgoogle.ai.generativelanguageaigenerativelanguagergoogle.generativeai.clientrrgoogle.generativeairrgoogle.generativeai.typesr r r r rr7rArMrRrfrhrvDEFAULT_DISCUSS_MODELrrrr version_infomajorminorDATACLASS_KWARGS prettyprintr dataclassrr~rrrrrrs           4  *1= c   @