o ߭j4 @sddlmZddlZddlmZmZddlZddlmZmZm Z m Z ddl m m ZddlmZddlmZddlmZddlmZdd lmZdd lmZdd lmZdd lmZd ZdZzejZWneyve dZdNddZYnwdOddZ eddddddddd dPd-d.Z!edddddddddd/ dQd6d7Z"ej#ej$d8d9Gd:d;d;ej%Z%  dRdSd>d?Z&  dRdTdAdBZ'e  dRdUdFdGZ(e  dRdVdJdGZ(  dRdWdMdGZ(dS)X) annotationsN)IterableSequence)AnyroverloadTypeVar)protos)get_default_text_client) string_utils) helper_types) text_types) model_types)models)palm_safety_typeszmodels/text-bison-001dTiterable Iterable[T]nintreturnIterable[list[T]]ccsX|dkr td|g}|D]}||t||kr"|Vg}q|r*|VdSdS)Nz Batch size `n` must be >1, got: ) ValueErrorappendlen)rrbatchitemrU/var/www/html/CropPilot/venv/lib/python3.10/site-packages/google/generativeai/text.py_batched+s   r promptstr | dict[str, str]protos.TextPromptcCs2t|tr tj|dSt|trt|Std)aV Creates a `protos.TextPrompt` object based on the provided prompt input. Args: prompt: The prompt input, either a string or a dictionary. Returns: protos.TextPrompt: A TextPrompt object containing the prompt text. Raises: TypeError: If the provided prompt is neither a string nor a dictionary. )textzKInvalid argument type: Expected a string or dictionary for the text prompt.) isinstancestrr TextPromptdict TypeErrorr!rrr_make_text_prompt9s   r+ modelr! temperaturecandidate_countmax_output_tokenstop_ptop_ksafety_settingsstop_sequencesr-model_types.AnyModelNameOptions str | Noner. float | Noner/ int | Noner0r1r2r3-palm_safety_types.SafetySettingOptions | Noner4str | Iterable[str] | Noneprotos.GenerateTextRequestc CsVt|}t|d}t|}t|tr|g}|rt|}tj |||||||||d S)a Creates a `protos.GenerateTextRequest` object based on the provided parameters. This function generates a `protos.GenerateTextRequest` object with the specified parameters. It prepares the input parameters and creates a request that can be used for generating text using the chosen model. Args: model: The model to use for text generation. prompt: The prompt for text generation. Defaults to None. temperature: The temperature for randomness in generation. Defaults to None. candidate_count: The number of candidates to consider. Defaults to None. max_output_tokens: The maximum number of output tokens. Defaults to None. top_p: The nucleus sampling probability threshold. Defaults to None. top_k: The top-k sampling parameter. Defaults to None. safety_settings: Safety settings for generated text. Defaults to None. stop_sequences: Stop sequences to halt text generation. Can be a string or iterable of strings. Defaults to None. Returns: `protos.GenerateTextRequest`: A `GenerateTextRequest` object configured with the specified parameters. r*r,) r make_model_namer+rnormalize_safety_settingsr%r&listrGenerateTextRequestr,rrr_make_generate_text_requestPs$ "   r@) r-r.r/r0r1r2r3r4clientrequest_optionsr&rAglm.TextServiceClient | NonerB&helper_types.RequestOptionsType | Nonetext_types.Completionc Cs(t|||||||||d } t| | | dS)ao Calls the API to generate text based on the provided prompt. Args: model: Which model to call, as a string or a `types.Model`. prompt: Free-form input text given to the model. Given a prompt, the model will generate text that completes the input text. 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`. max_output_tokens: Maximum number of tokens to include in a candidate. Must be greater than zero. If unset, will default to 64. 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]`. safety_settings: A list of unique `types.SafetySetting` instances for blocking unsafe content. These will be enforced on the `prompt` and `candidates`. There should not be more than one setting for each `types.SafetyCategory` type. The API will block any prompts and responses that fail to meet the thresholds set by these settings. This list overrides the default settings for each `SafetyCategory` specified in the safety_settings. If there is no `types.SafetySetting` for a given `SafetyCategory` provided in the list, the API will use the default safety setting for that category. stop_sequences: A set of up to 5 character sequences that will stop output generation. If specified, the API will stop at the first appearance of a stop sequence. The stop sequence will not be included as part of the response. client: If you're not relying on a default client, you pass a `glm.TextServiceClient` instead. request_options: Options for the request. Returns: A `types.Completion` containing the model's text completion response. r,)rArequestrB)r@_generate_response) r-r!r.r/r0r1r2r3r4rArBrFrrr generate_texts: rHF)initc@seZdZddZdS) CompletioncKsB|D] \}}t|||qd|_|jr|jdd|_dSdS)Nroutput)itemssetattrresult candidates)selfkwargskeyvaluerrr__init__s zCompletion.__init__N)__name__ __module__ __qualname__rTrrrrrJs rJrFglm.TextServiceClientcCs|duri}|dur t}|j|fi|}t||}t|d|d<t|d|d<t|d|d<tdd|i|S)a Generates a response using the provided `protos.GenerateTextRequest` and client. Args: request: The text generation request. client: The client to use for text generation. Defaults to None, in which case the default text client is used. request_options: Options for the request. Returns: `Completion`: A `Completion` object with the generated text and response information. Nfilterssafety_feedbackrO_clientr) r rHtypeto_dictrconvert_filters_to_enums convert_safety_feedback_to_enumsconvert_candidate_enumsrJ)rFrArBresponserrrrGsrGtext_types.TokenCountcCsRt|}|dur i}|durt}|jtj|d|idfi|}t||S)z?Calls the API to count the number of tokens in the text prompt.Nr$)r-r!)rget_base_model_namer count_text_tokensrCountTextTokensRequestr\r])r-r!rArB base_modelrNrrrrds rd model_types.BaseModelNameOptionsr$text_types.EmbeddingDictcCdSNrr-r$rArBrrrgenerate_embeddingsrl Sequence[str]text_types.BatchEmbeddingDictcCrirjrrkrrrrl rmstr | Sequence[str]8text_types.EmbeddingDict | text_types.BatchEmbeddingDictc Cst|}|dur i}|durt}t|tr8tj||d}|j|fi|}t| |}|dd|d<|Sdgi}t |t D]'}tj ||d}|j |fi|}t| |}|ddd|dDqA|S) a$Calls the API to create an embedding for the text passed in. Args: model: Which model to call, as a string or a `types.Model`. text: Free-form input text given to the model. Given a string, the model will generate an embedding based on the input text. client: If you're not relying on a default client, you pass a `glm.TextServiceClient` instead. request_options: Options for the request. Returns: Dictionary containing the embedding (list of float values) for the input text. N)r-r$ embeddingrS)r-textscss|]}|dVqdS)rSNr).0errr Xsz&generate_embeddings.. embeddings)r r<r r%r&rEmbedTextRequest embed_textr\r]r EMBEDDING_MAX_BATCH_SIZEBatchEmbedTextRequestbatch_embed_textextend) r-r$rArBembedding_requestembedding_responseembedding_dictrNrrrrrl)s4  )rrrrrr)r!r"rr#)r-r5r!r6r.r7r/r8r0r8r1r8r2r8r3r9r4r:rr;)r-r5r!r&r.r7r/r8r0r8r1r7r2r7r3r9r4r:rArCrBrDrrE)NN)rFr;rArXrBrDrrJ) r-r5r!r&rArCrBrDrrb) r-rgr$r&rArXrBrDrrh) r-rgr$rnrArXrBrDrro) r-rgr$rprArXrBrDrrq)) __future__r dataclassescollections.abcrr itertoolstypingrrrgoogle.ai.generativelanguageaigenerativelanguageglmgoogle.generativeairgoogle.generativeai.clientr r google.generativeai.typesr r r rrDEFAULT_TEXT_MODELrzbatchedr AttributeErrorrr+r@rH prettyprint dataclassrJrGrdrlrrrrs            9I   &