9iu ddlZddlZddlZddlZddlZddlZddlZddlmZm Z m Z m Z m Z ddl mZmZmZmZddlmZmZmZddlmZmZmZddlmZddlmZdd lmZm Z m!Z!m"Z"m#Z#m$Z$d d l%m&Z&m'Z'e$jPe)Z*e"rddl+Z+e!rdd l,m-Z-d Z.d*dZ/dZ0GddZ1Gdde1Z2Gdde1Z3de1fdZ4dZ5dddddddd d!d"d# Z6d$Z7d%Z8d+d&Z9d'Z:Gd(d)Z;y),N)AnyDictListOptionalUnion) create_repohf_hub_downloadmetadata_update upload_folder)RepositoryNotFoundErrorbuild_hf_headers get_session)custom_object_saveget_class_from_dynamic_module get_imports) is_pil_image) AutoProcessor) CONFIG_NAME cached_fileis_accelerate_availableis_torch_availableis_vision_availablelogging)handle_agent_inputshandle_agent_outputs)send_to_devicetool_config.jsonc ||S t|tfddi|y#t$r> t|tfddi|Yy#t$rtd|dt$rYYywxYwt$rYywxYw)N repo_typespacemodel`z9` does not seem to be a valid repo identifier on the Hub.)r TOOL_CONFIG_FILEr EnvironmentError Exception)repo_idr! hub_kwargss a/var/www/html/backtest/airagagent/rag_env/lib/python3.12/site-packages/transformers/tools/base.py get_repo_typer+7s !1SWS S "  G%5 W WJ W& k"Qwi/h#ij j   s* A+9 AA+A A+*A+zufrom transformers import launch_gradio_demo from {module_name} import {class_name} launch_gradio_demo({class_name}) c eZdZUdZdZeed<dZeed<eeed<eeed<dZ d Z d Z d Z e dd edeedeedefdZ dd ededeedeeeefdedef dZedZy )Toola A base class for the functions used by the agent. Subclass this and implement the `__call__` method as well as the following class attributes: - **description** (`str`) -- A short description of what your tool does, the inputs it expects and the output(s) it will return. For instance 'This is a tool that downloads a file from a `url`. It takes the `url` as input, and returns the text contained in the file'. - **name** (`str`) -- A performative name that will be used for your tool in the prompt to the agent. For instance `"text-classifier"` or `"image_generator"`. - **inputs** (`List[str]`) -- The list of modalities expected for the inputs (in the same order as in the call). Modalitiies should be `"text"`, `"image"` or `"audio"`. This is only used by `launch_gradio_demo` or to make a nice space from your tool. - **outputs** (`List[str]`) -- The list of modalities returned but the tool (in the same order as the return of the call method). Modalitiies should be `"text"`, `"image"` or `"audio"`. This is only used by `launch_gradio_demo` or to make a nice space from your tool. You can also override the method [`~Tool.setup`] if your tool as an expensive operation to perform before being usable (such as loading a model). [`~Tool.setup`] will be called the first time you use your tool, but not at instantiation. zThis is a tool that ... descriptionnameinputsoutputscd|_y)NFis_initializedselfargskwargss r*__init__z Tool.__init__ms #ctdS)Nz-Write this method in your subclass of `Tool`.)NotImplementedr6s r*__call__z Tool.__call__psMNNr;cd|_y)z Overwrite this method here for any operation that is expensive and needs to be executed before you start using your tool. Such as loading a big model. TNr4)r7s r*setupz Tool.setupss #r;ctj|d|jdk(rtd|d|dt ||}|j j}|j dd}|d|j j}tjj|d }tjj|r-t|d d 5}tj|}d d d ni}||j|jd}t|dd 5}|j!tj"|dddzd d d tjj|d} t| dd 5}|j!t$j'||j jd d d tjj|d} g} |D]} | j)t+| t-t/| } t| dd 5}|j!dj| dzd d d y #1swYhxYw#1swYxYw#1swYxYw#1swYy xYw)a Saves the relevant code files for your tool so it can be pushed to the Hub. This will copy the code of your tool in `output_dir` as well as autogenerate: - a config file named `tool_config.json` - an `app.py` file so that your tool can be converted to a space - a `requirements.txt` containing the names of the module used by your tool (as detected when inspecting its code) You should only use this method to save tools that are defined in a separate module (not `__main__`). Args: output_dir (`str`): The folder in which you want to save your tool. T)exist_ok__main__z We can't save the code defining z in z{ as it's been defined in __main__. You have to put this code in a separate module so we can include it in the saved folder..rrutf-8encodingN) tool_classr.r0wr)indent sort_keys zapp.py) module_name class_namezrequirements.txt)osmakedirs __module__ ValueErrorr __class__split__name__pathjoinisfileopenjsonloadr.r0writedumpsAPP_FILE_TEMPLATEformatextendrlistset) r7 output_dir module_filesrO last_module full_name config_filef tool_configapp_filerequirements_fileimportsmodules r*savez Tool.savezsH J. ??j (24&ZLIgg *$ ; nn// !'',R0 "m1T^^%<%<$=> ggll:/AB 77>>+ &k39 +Q"iil  + +K%.t?O?OY]YbYbc +sW 5 N GGDJJ{1EL M N77<< H5 (C' 2 ka GG%,,QUQ_Q_QhQh,i j kGGLL5GH" 0F NN;v. / 0s7|$ #S7 ; /q GGDIIg&- . / /) + +  N N  k k / /s0I +I#;I0)$I<I #I-0I9<JNr( model_repo_idtokenremotec |r$|"t}||vrtd|d||}gd}|jD cic] \}} ||vs || } }} t|fi| | d<t |t fd|i| ddd} | du} | t |t fd|i| ddd} | t|d t| d 5} tj| }ddd| sd vrt|d |d }n}|d}t||fd|i| }t|jdk(r |d|_ |j|dk7r,tj|j d|d|_ t|j"dk(r |d|_|j"|dk7r,tj|j d|d|_|rt%|||S||fd|i|Scc} }w#1swYxYw)aw Loads a tool defined on the Hub. Args: repo_id (`str`): The name of the repo on the Hub where your tool is defined. model_repo_id (`str`, *optional*): If your tool uses a model and you want to use a different model than the default, you can pass a second repo ID or an endpoint url to this argument. token (`str`, *optional*): The token to identify you on hf.co. If unset, will use the token generated when running `huggingface-cli login` (stored in `~/.huggingface`). remote (`bool`, *optional*, defaults to `False`): Whether to use your tool by downloading the model or (if it is available) with an inference endpoint. kwargs (additional keyword arguments, *optional*): Additional keyword arguments that will be split in two: all arguments relevant to the Hub (such as `cache_dir`, `revision`, `subfolder`) will be used when downloading the files for your tool, and the others will be passed along to its init. N'Could not infer a default endpoint for :, you need to pass one using the `model_repo_id` argument.) cache_dirforce_downloadresume_downloadproxiesrevisionr! subfolderlocal_files_onlyr!use_auth_tokenF)%_raise_exceptions_for_missing_entries'_raise_exceptions_for_connection_errorszY does not appear to provide a valid configuration in `tool_config.json` or `config.json`.rGrH custom_toolzO does not provide a mapping to custom tools in its configuration `config.json`.rJrr0z_ implements a different name in its configuration and class. Using the tool configuration name.r.zm implements a different description in its configuration and class. Using the tool configuration description.rrrJrr)get_default_endpointsrTitemsr+rr%rr&r[r\r]rlenr0loggerwarningrWr. RemoteTool)clsr(rqrrrsr9 endpointshub_kwargs_nameskvr)resolved_config_fileis_tool_configreaderconfigrrJs r*from_hubz Tool.from_hubs8 m+-/Ii' =gYG00&g.M  (.||~Otq!>N9NadO O#0"F:"F ;*   !   3849  .T9  '#.$ %$ $ 7<8= $  '")tu & 9 'VYYv&F 'F*&ino!/K K . 2:wkW\k`jk z 1 $)&1JO ??k&1 1 NN&&'(&& *&1JO z%% &! +%0%?J "  ! ![%? ? NN&&'(22 &1%?J " m5ZP P-?u???}P6 ' 's G G5G$$G.commit_messageprivate create_prreturnc pt|||ddd}|j}t|ddgidtj5}|j |t jd|d d jtj|t|||||d cd d d S#1swYy xYw) a Upload the tool to the Hub. Parameters: repo_id (`str`): The name of the repository you want to push your tool to. It should contain your organization name when pushing to a given organization. commit_message (`str`, *optional*, defaults to `"Upload tool"`): Message to commit while pushing. private (`bool`, *optional*): Whether or not the repository created should be private. token (`bool` or `str`, *optional*): The token to use as HTTP bearer authorization for remote files. If unset, will use the token generated when running `huggingface-cli login` (stored in `~/.huggingface`). create_pr (`bool`, *optional*, defaults to `False`): Whether or not to create a PR with the uploaded files or directly commit. Tr"gradio)r(rrrrBr! space_sdktagstoolr!z!Uploading the following files to z: ,)r(r folder_pathrrrr!N) rr(r tempfileTemporaryDirectoryrprinforYrQlistdirr )r7r(rrrrrrepo_urlwork_dirs r* push_to_hubzTool.push_to_hubs25'DT[go ""&6(!3wG  ( ( * h IIh  KK;G9BsxxPRPZPZ[cPdGeFfg h -$#!    s AB,,B5cRGddt}|j|_||S)z8 Creates a [`Tool`] from a gradio tool. ceZdZfdZxZS)+Tool.from_gradio..GradioToolWrappercft||j|_|j|_yN)superr:r0r.)r7 _gradio_toolrUs r*r:z4Tool.from_gradio..GradioToolWrapper.__init__Qs) "(-- #/#;#; r;)rWrS __qualname__r: __classcell__rUs@r*GradioToolWrapperrPs  < ) gradio_toolrs r* from_gradiozTool.from_gradioJs(  < < &1__" --r;)NNF)z Upload toolNNF)rWrSr__doc__r.str__annotations__r0rr:r>r@rp classmethodrboolrrr staticmethodrr;r*r-r-Qs*1K0D#N I #Y$O#4/l(,# k@k@ }k@} k@  k@k@`,"&,0 ***$ * dCi() *  * *X . .r;r-c*eZdZdZddZdZdZdZy)rat A [`Tool`] that will make requests to an inference endpoint. Args: endpoint_url (`str`, *optional*): The url of the endpoint to use. token (`str`, *optional*): The token to use as HTTP bearer authorization for remote files. If unset, will use the token generated when running `huggingface-cli login` (stored in `~/.huggingface`). tool_class (`type`, *optional*): The corresponding `tool_class` if this is a remote version of an existing tool. Will help determine when the output should be converted to another type (like images). NcD||_t|||_||_y)Nrr) endpoint_urlEndpointClientclientrJ)r7rrrrJs r*r:zRemoteTool.__init__is($\? $r;c |j}t|dkDr|j6t|jtr|jj }n|jj }tj|j}|jDcgc]G\}}|jtjjtjjfvr|I}}}|ddk(r|dd}t|t|kDr0t|jdt|dt|dt!||D] \} } | || < n\t|dkDr tdt|dk(r5t#|dr d |j$j'|diSd |diS|jD]/\} } t#| s|j$j'| || <1d |iScc}}w) aP Prepare the inputs received for the HTTP client sending data to the endpoint. Positional arguments will be matched with the signature of the `tool_class` if it was provided at instantation. Images will be encoded into bytes. You can override this method in your custom class of [`RemoteTool`]. rNr7rz only accepts z arguments but z were given.z4A `RemoteTool` can only accept one positional input.r1)copyrrJ issubclass PipelineToolencoder>inspect signature parametersrkind_ParameterKindVAR_POSITIONAL VAR_KEYWORDrTziprr encode_image) r7r8r9r1 call_methodrrprargr0keyvalues r*prepare_inputszRemoteTool.prepare_inputsns t9q=*doo|<"&//"8"8K"&//":":K#--k:EE !* 11vvg&<&<&K&KWMcMcMoMo%pp  a=F*!+ABJt9s:.$??+>#j/9J/Z]^bZcYddpq"%T:!6'IC#&F4L'TQ !WXXTaQ($dkk&>&>tAw&GHH $q'** ,,. >JCE""kk66u=s  >&!!1s&A Hc|S)z You can override this method in your custom class of [`RemoteTool`] to apply some custom post-processing of the outputs of the endpoint. rr7r2s r*extract_outputszRemoteTool.extract_outputss r;ct|i|\}}|jduxr|jjdgk(}|j|i|}t |t r|j di|d|i}n|j ||}t |tr&t|dk(rt |dtr|d}t||j|jjnd}|j|S)Nimage output_image)rrrr) rrJr2r isinstancedictrrcrrr)r7r8r9rr1r2s r*r>zRemoteTool.__call__s*D;F; fd2[t7N7NSZR[7[ $$$d5f5 fd #!dkkFFFFGkk&|kDG gt $W):z'RS*VZ?[ajG&w4??Kf0G0Glpq##G,,r;)NNN)rWrSrrr:rrr>rr;r*rrZs % )"V-r;rcdeZdZdZeZdZeZdZ d fd Z fdZ dZ dZ dZ dZxZS) ra0 A [`Tool`] tailored towards Transformer models. On top of the class attributes of the base class [`Tool`], you will need to specify: - **model_class** (`type`) -- The class to use to load the model in this tool. - **default_checkpoint** (`str`) -- The default checkpoint that should be used when the user doesn't specify one. - **pre_processor_class** (`type`, *optional*, defaults to [`AutoProcessor`]) -- The class to use to load the pre-processor - **post_processor_class** (`type`, *optional*, defaults to [`AutoProcessor`]) -- The class to use to load the post-processor (when different from the pre-processor). Args: model (`str` or [`PreTrainedModel`], *optional*): The name of the checkpoint to use for the model, or the instantiated model. If unset, will default to the value of the class attribute `default_checkpoint`. pre_processor (`str` or `Any`, *optional*): The name of the checkpoint to use for the pre-processor, or the instantiated pre-processor (can be a tokenizer, an image processor, a feature extractor or a processor). Will default to the value of `model` if unset. post_processor (`str` or `Any`, *optional*): The name of the checkpoint to use for the post-processor, or the instantiated pre-processor (can be a tokenizer, an image processor, a feature extractor or a processor). Will default to the `pre_processor` if unset. device (`int`, `str` or `torch.device`, *optional*): The device on which to execute the model. Will default to any accelerator available (GPU, MPS etc...), the CPU otherwise. device_map (`str` or `dict`, *optional*): If passed along, will be used to instantiate the model. model_kwargs (`dict`, *optional*): Any keyword argument to send to the model instantiation. token (`str`, *optional*): The token to use as HTTP bearer authorization for remote files. If unset, will use the token generated when running `huggingface-cli login` (stored in `~/.huggingface`). hub_kwargs (additional keyword arguments, *optional*): Any additional keyword argument to send to the methods that will load the data from the Hub. Nc rts tdts td|#|j t d|j}||}||_||_||_||_||_ |in||_ |||jd<||_ ||jd<t |5y)N/Please install torch in order to use this tool.z4Please install accelerate in order to use this tool.zHThis tool does not implement a default checkpoint, you need to pass one. device_maprr)r ImportErrorrdefault_checkpointrTr# pre_processorpost_processordevicer model_kwargsr)rr:) r7r#rrrrrrrr)rUs r*r:zPipelineTool.__init__s"#OP P&(TU U =&&. !kll++E  !M *, $"."6BL  !.8D  l +$#(  r;crt|jtr6|jj|jfi|j |_t|j trB|jj|j fi|j|j |_|j|j|_ nPt|jtr6|jj|jfi|j |_ |jQ|j6t|j jjd|_ nt!|_ |j%|j j#|jt$|My)z^ Instantiates the `pre_processor`, `model` and `post_processor` if necessary. Nr)rrrpre_processor_classfrom_pretrainedr)r# model_classrrpost_processor_classrrrc hf_device_mapvaluesget_default_devicetorr@)r7rUs r*r@zPipelineTool.setupsE d((# .!I!9!9!I!I$J\J\!p`d`o`o!pD  djj# &9))99$**mHYHYm]a]l]lmDJ    &"&"4"4D  ++S 1"K$";";"K"KDL_L_"scgcrcr"sD  ;; *"4::#;#;#B#B#DEaH 02 ?? " JJMM$++ &  r;c$|j|S)zQ Uses the `pre_processor` to prepare the inputs for the `model`. )r)r7 raw_inputss r*rzPipelineTool.encodes!!*--r;cztj5|jdi|cdddS#1swYyxYw)z7 Sends the inputs through the `model`. Nr)torchno_gradr#)r7r1s r*forwardzPipelineTool.forward!s3]]_ (4::'' ( ( (s1:c$|j|S)zG Uses the `post_processor` to decode the model output. )rrs r*decodezPipelineTool.decode(s""7++r;c.t|i|\}}|js|j|j|i|}t ||j }|j |}t |d}|j|}t||jS)Ncpu) rr5r@rrrrrrr2)r7r8r9encoded_inputsr2decoded_outputss r*r>zPipelineTool.__call__.s*D;F; f"" JJL$d5f5' D,,~. %0++g.#OT\\BBr;)NNNNNNN)rWrSrrrrrrrr:r@rrrr>rrs@r*rrsY#J(K(#J4. (, Cr;rrJc ddl}|fd}|j||j|j|j j jy#t$r tdwxYw)z Launches a gradio demo for a tool. The corresponding tool class needs to properly implement the class attributes `inputs` and `outputs`. Args: tool_class (`type`): The class of the tool for which to launch the demo. rNz.fnLsT$V$$r;)rr1r2titlearticle)rr Interfacer1r2rWr.launch)rJgrrrs @r*launch_gradio_demor=s|Z .inners&MM  r;r)r.r+s` r*add_descriptionr,s  Lr;c eZdZd dedeefdZedZedZ ddee ee e ee e efdee d ee d e d ef d Zy)rNrrrc@it|ddi|_||_y)Nrz Content-Typezapplication/json)r headersr)r7rrrs r*r:zEndpointClient.__init__s$\*7\I[\ (r;ctj}|j|dtj|j }|j dS)NPNG)rarG)ioBytesIOrpbase64 b64encodegetvaluer)r_bytesb64s r*rzEndpointClient.encode_imagesC 6% (v01zz'""r;cts tdddlm}t j |}t j|}|j|S)NzbThis tool returned an image but Pillow is not installed. Please install it (`pip install Pillow`).r)Image) rrPILr:r4 b64decoder2r3r[) raw_imager:r8r7s r* decode_imagezEndpointClient.decode_imagesI"$t  y)Czz&!!r;r1paramsdatarrci}|r||d<|r||d<tj|j|j||}|r|j |j S|j S)Nr1r)r/r\r@)rpostrr/r>contentr\)r7r1r?r@rpayloadresponses r*r>zEndpointClient.__call__st  &GH  $*GL !=%%d&7&7T[bf%g $$X%5%56 6==? "r;r)NNNF)rWrSrrrr:rrr>rrrbytesrrr>rr;r*rrs)S)#)##  " "JN!% $" #sD$s)T$s)_DEF##uo #  # #r;rr)NFN)rQs " 33XXXXaa&'C   H %/&&F.F.RT-T-nIC4ICX48 #"$C- <1(,3:($  ( 3ql .#.#r;