o ZhQ @sddlmZddlZddlZddlZddlmZmZmZm Z m Z ddl m m ZddlmZddlmZddlmZddlmZddlmZdd lmZd]ddZd^ddZd_ddZd`ddZdad d!Z dbdddd"dcd*d+Z dbdddd"dcd,d-Zddddddddd.ddd9d:Zd;Z d;ddddddddddd< dedBdCZ!e"e!j#d;ddddddddddd< dfdEdFZ$ej%j&ej%j'fdGkrdHdIiZ(niZ(ej)e"ej*j#ej+dgie(dJdKiGdLdMdMej*Z*dhdRdSZ,  didjdTdUZ-  didkdVdWZ.dddde dddXdld[d\Z/dS)m) annotationsN)AnyIterableListOptionalUnion)get_default_discuss_client) get_default_discuss_async_client) string_utils) discuss_types) model_types) safety_typescontentdiscuss_types.MessageOptionsreturn glm.MessagecCs0t|tjr|St|trtj|dSt|S)z9Creates a `glm.Message` object from the provided content.r) isinstanceglmMessagestrrrR/var/www/html/lang_env/lib/python3.10/site-packages/google/generativeai/discuss.py _make_message!s    rmessagesdiscuss_types.MessagesOptionsList[glm.Message]cCst|tttjfrt|g}ndd|D}tdd|dddD}|s)d}nt|dkr4|}nt d td d|dddD}|sLd }nt|dkrW|}nt d t d d|Drg|S||g}t |D] \}}||d|_ qo|S) a' Creates a list of `glm.Message` objects from the provided messages. This function takes a variety of message content inputs, such as strings, dictionaries, or `glm.Message` objects, and creates a list of `glm.Message` objects. It ensures that the authors of the messages alternate appropriately. If authors are not provided, default authors are assigned based on their position in the list. Args: messages: The messages to convert. Returns: A list of `glm.Message` objects with alternating authors. cSsg|]}t|qSr)r).0messagerrr ?sz"_make_messages..cs|] }|jr|jVqdSNauthorrmsgrrr Az!_make_messages..N0z$Authors are not strictly alternatingcsr r!r"r$rrrr&Ir'1css|]}|jVqdSr!r"r$rrrr&Qs)rrdictrrrsetlenpopr Z AuthorErrorall enumerater#)rZ even_authorsZ even_authorZ odd_authorsZ odd_authorZauthorsir%rrr_make_messages+s*       r3itemdiscuss_types.ExampleOptions glm.ExamplecCst|tjr|St|tr&|}t|d|d<t|d|d<t|St|trresultpairnr4r%Zprimerrrr_make_examples_from_flatns,   rHdiscuss_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 `glm.Example` objects from the provided examples. This function takes various types of example content inputs and creates a list of `glm.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 `glm.Example` objects created from the provided examples. rrr7r8zTTo create an `Example` from a dict you must supply both `input` and an `output` keys) rrr:r,r=r<rH TypeErrorr ZMESSAGE_OPTIONSrD)r>firstrEr4rrr_make_exampless,     rLcontextr>rprompt"discuss_types.MessagePromptOptionsrN str | None$discuss_types.ExamplesOptions | None$discuss_types.MessagesOptions | Noneglm.MessagePromptcCs|dur t|||d}n$|dup|dup|du}|rtdt|tjr&|St|tr,nd|i}t|}|tj sFt d|tj | dd}|durVt ||d<| dd}|durft ||d<dd|D}|S) a Creates a `glm.MessagePrompt` object from the provided prompt components. This function constructs a `glm.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 `glm.MessagePrompt` object created from the provided prompt components. NrMzWYou can't set `prompt`, and its fields `(context, examples, messages)` at the same timerz.Found extra entries in the prompt dictionary: r>cSsi|] \}}|dur||qSr!r)rkvrrr sz-_make_message_prompt_dict..)r,rArr MessagePromptr-keysissubsetr ZMESSAGE_PROMPT_KEYSKeyErrorgetrLr3items)rOrNr>rZ flat_promptrYrrr_make_message_prompt_dicts:        r^cCst||||d}t|S)zICreates a `glm.MessagePrompt` object from the provided prompt components.rOrNr>r)r^rrXr_rrr_make_message_prompts r`)rNr>r temperaturecandidate_counttop_ptop_krOmodel&model_types.AnyModelNameOptions | Nonera float | Nonerb int | Nonercrd)discuss_types.MessagePromptOptions | Noneglm.GenerateMessageRequestc Cs0t|}t||||d}tj||||||dS)zFCreates a `glm.GenerateMessageRequest` object for generating messages.r_)rerOrarcrdrb)r make_model_namer`rZGenerateMessageRequest rerNr>rrarbrcrdrOrrr_make_generate_message_requests rmzmodels/chat-bison-001) rerNr>rrarbrcrdrOclientrequest_optionsrnglm.DiscussServiceClient | Nonerodict[str, Any] | Nonediscuss_types.ChatResponsec Cs(t|||||||||d } t| | | dS)aU Calls the API and returns a `types.ChatResponse` containing the response. 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. rlrnrequestro)rm_generate_response rerNr>rrarbrcrdrOrnrortrrrchat3sT rw$glm.DiscussServiceAsyncClient | Nonec s0t|||||||||d } t| | | dIdHS)Nrlrs)rm_generate_response_asyncrvrrr chat_asyncs  rz) kw_onlyTinitFc@seZdZUejddddZded<ddZee e j j jdd d Z e jddd Z e e j jj ddddZe e j jjdddZdS) ChatResponsecCsdSr!rrrrrszChatResponse.F)defaultreprrp_clientcKs"|D] \}}t|||qdSr!)r]setattr)selfkwargskeyvaluerrr__init__szChatResponse.__init__rrQcCs|jdr |jddSdS)Nr)r)rrrrlasts zChatResponse.lastrrcCs$t|}t||}||jd<dS)Nr)rtypeto_dictr)rrrrrrsNrorqrrcCst|jtjr td|jdurtd|j|}| d| ddt |d|d<|d t |t di|}t||j|dS)NzBreply can't be called on an async client, use reply_async instead.zThe last response from the model did not return any candidates. Check the `.filters` attribute to see why the responses were filtered: candidatesfiltersr)rtrnror)rrrZDiscussServiceAsyncClientrJrrArrr/r<rDrrmru)rrrortrrrreplys"   zChatResponse.replycszt|jtjr td|}|d|ddt|d|d<|dt |t di|}t ||jdIdHS)NzEreply_async can't be called on a non-async client, use reply instead.rrr)rtrnr) rrrZDiscussServiceClientrJrr/r<rDrrmry)rrrtrrr reply_asyncs  zChatResponse.reply_async)rrQ)rrr!)rrrorqrrr)rrrrr)__name__ __module__ __qualname__ dataclassesfieldr__annotations__rpropertyr set_docr rr__doc__setterrrrrrrrs  rrtresponseglm.GenerateMessageResponse8glm.DiscussServiceClient | glm.DiscussServiceAsyncClientcCst||}|d}|d|d<|d|d<|d|d<t||}|dt|d|d<|dr>|dd}nd}|d||dd|d dtd d |i||S) NrOr>rNrrrrrarbrr)rrr/r Zconvert_filters_to_enumsrD setdefaultr)rtrrnrOrrrr_build_chat_responses       rcCs8|duri}|dur t}|j|fi|}t|||Sr!)rgenerate_messagerrtrnrorrrrrus  rucs@|duri}|durt}|j|fi|IdH}t|||Sr!)r rrrrrrry)s ry)rOrNr>rrernromodel_types.AnyModelNameOptionsdiscuss_types.TokenCountcCsXt|}t||||d}|duri}|durt}|jd||d|}t||S)NrM)rerOr)r rkr`rcount_message_tokensrr)rOrNr>rrernrorErrrr9s r)rrrr)rrrr)r4r5rr6)r>r?rr@)r>rIrr@r!) rOrPrNrQr>rRrrSrrT)rerfrNrQr>rRrrSrargrbrhrcrgrdrgrOrirrj)rerfrNrQr>rRrrSrargrbrhrcrgrdrgrOrirnrprorqrrr)rerfrNrQr>rRrrSrargrbrhrcrgrdrgrOrirnrxrorqrrrr)rtrjrrrnrrr)NN)rtrjrnrprorqrr)rtrjrnrxrorqrr)rOrPrNrQr>rRrrSrerrnrxrorqrr)0 __future__rrsysrBtypingrrrrrZgoogle.ai.generativelanguageZaiZgenerativelanguagerZgoogle.generativeai.clientrr Zgoogle.generativeair Zgoogle.generativeai.typesr r r rr3r=rHrLr^r`rmZDEFAULT_DISCUSS_MODELrwrrrz version_infomajorminorZDATACLASS_KWARGSZ prettyprintr dataclassrruryrrrrrs         0  *1> c   ?