o wi:@sddlmZmZddlmZddlmZddlmZm Z m Z m Z m Z m Z ddlmZmZddlmZGdd d e d d ZGd d d eZGddde d d ZGddde ZdgZdS))OptionalUnion) BatchFeature) ImageInput) ImagesKwargsMultiModalDataProcessingKwargsProcessorMixinUnpack VideosKwargs)PreTokenizedInput TextInput) VideoInputc@s"eZdZUeeeefed<dS)Glm4vVideosProcessorKwargsfpsN)__name__ __module__ __qualname__rlistfloat__annotations__rrg/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/transformers/models/glm4v/processing_glm4v.pyrs rF)totalc@s2eZdZUeeed<eeed<eeed<dS)Glm4vImagesKwargs patch_sizetemporal_patch_size merge_sizeN)rrrrintrrrrrr#s  rc@s*eZdZUeed<eed<dddiiZdS)Glm4vProcessorKwargs images_kwargs videos_kwargs text_kwargspaddingFN)rrrrrr _defaultsrrrrr )s r c seZdZdZgdZdZdZdZdfdd Z   dd e d e e e e e e e fd ed eed ef ddZdddZddZddZ dddZeddZZS) Glm4vProcessora Constructs a GLM-4V processor which wraps a GLM-4V image processor and a GLM-4 tokenizer into a single processor. [`~Glm4vProcessor.__call__`] and [`~Glm4vProcessor.decode`] for more information. Args: image_processor ([`Glm4vProcessor`], *optional*): The image processor is a required input. tokenizer ([`PreTrainedTokenizerFast`], *optional*): The tokenizer is a required input. video_processor ([`Glm4vVideoProcessor`], *optional*): The video processor is a required input. chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages in a chat into a tokenizable string. )image_processor tokenizervideo_processorAutoImageProcessorAutoVideoProcessor)PreTrainedTokenizerPreTrainedTokenizerFastNc stj||||dt|dsdn|j|_t|dsdn|j|_t|ddr)|jn||j|_t|ddr<|j|_dS||j|_dS)N) chat_template image_tokenz <|image|> video_tokenz <|video|>image_token_idvideo_token_id) super__init__hasattrr/r0getattrr1convert_tokens_to_idsr2)selfr'r(r)r.kwargs __class__rrr4Is    zGlm4vProcessor.__init__imagestextvideosr9returncKs6|jtfd|jji|}|dur"|jdd|i|d}|d}ni}d}|dur@|jdd|i|d}|d} |d } ni}g} d} t|tsN|g}| }|dur|jj d } d } t t |D]5} |j || vr|| | }|| |j d |d || <| d 7} |j || vsm|| d |j || <qd| durr|jj d } d }t t |D]} |j|| vrf| |d }d}t| dr| d }n t| d tr| d n| }g}t d t |D] }|||q|d|}t ||kr ||r|dnd t ||kst |D]}||}d|j d|}||7}q|| |j|d || <| || | |d }t |D]}|j || vrY|| |j d |d || <qA|d 7}|j|| vs|| d |j || <q|ddd}|j|fi|d}|j||ddgdti||||dS)a^ Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text` and `kwargs` arguments to PreTrainedTokenizerFast's [`~PreTrainedTokenizerFast.__call__`] if `text` is not `None` to encode the text. Args: images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`): The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch tensor. Both channels-first and channels-last formats are supported. text (`str`, `List[str]`, `List[List[str]]`): The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set `is_split_into_words=True` (to lift the ambiguity with a batch of sequences). videos (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`): The image or batch of videos to be prepared. Each video can be a 4D NumPy array or PyTorch tensor, or a nested list of 3D frames. Both channels-first and channels-last formats are supported. return_tensors (`str` or [`~utils.TensorType`], *optional*): If set, will return tensors of a particular framework. Acceptable values are: - `'tf'`: Return TensorFlow `tf.constant` objects. - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return NumPy `np.ndarray` objects. - `'jax'`: Return JAX `jnp.ndarray` objects. Returns: [`BatchFeature`]: A [`BatchFeature`] with the following fields: - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not `None`). - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`. - **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`. - **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`. - **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`. tokenizer_init_kwargsNr<r!image_grid_thwr>r" timestampsvideo_grid_thwrz<|placeholder|>tolistz<|begin_of_image|>z<|end_of_image|>r#return_tensorsimagevideo) modalities)data tensor_typer) _merge_kwargsr r( init_kwargsr'r)pop isinstancercopyrrangelenr/prodreplacer0r5rGappend_check_special_mm_tokensr)r8r<r=r>r9 output_kwargs image_inputsrA videos_inputsrBrC merge_lengthindexinum_image_tokens video_index num_framesvideo_structuretimestamps_listunique_timestampsidxselected_timestamps frame_idx timestamp_secframe_structurerI text_inputsrrr__call__Xs*              zGlm4vProcessor.__call__c  si}|dur7tjdi|ddpjjfdd|D}fdd|D}|||d|dur^tjdi|fd d|D}fd d|D}||d <td i|S) aK Computes the number of placeholder tokens needed for multimodal inputs with the given sizes. Args: image_sizes (`list[list[int]]`, *optional*): The input sizes formatted as (height, width) per each image. video_sizes (`list[list[int]]`, *optional*): The input sizes formatted as (num_frames, height, width) per each video. Returns: `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided input modalities, along with other useful data. Nr!rcs"g|] }jjg|RqSr)r'get_number_of_image_patches).0 image_size)r!r8rr z=Glm4vProcessor._get_num_multimodal_tokens..cg|]}|dqSrDrrn num_patchesrrrrp)r`num_image_patchesr"cs"g|] }jjg|RqSr)r)get_number_of_video_patches)rn video_size)r8r"rrrprqcrrrsrrtrvrrrprwnum_video_tokensr)r r%getupdater'rr) r8 image_sizes video_sizesr9 vision_datarxr`num_video_patchesr{r)r!rr8r"r_get_num_multimodal_tokenss&     z)Glm4vProcessor._get_num_multimodal_tokenscO|jj|i|S)z This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please refer to the docstring of this method for more information. r( batch_decoder8argsr9rrrrzGlm4vProcessor.batch_decodecOr)z This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to the docstring of this method for more information. )r(decoderrrrrrzGlm4vProcessor.decodeTFcKs|jj|f||d|S)a Post-process the output of the model to decode the text. Args: generated_outputs (`torch.Tensor` or `np.ndarray`): The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)` or `(sequence_length,)`. skip_special_tokens (`bool`, *optional*, defaults to `True`): Whether or not to remove special tokens in the output. Argument passed to the tokenizer's `batch_decode` method. clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`): Whether or not to clean up the tokenization spaces. Argument passed to the tokenizer's `batch_decode` method. **kwargs: Additional arguments to be passed to the tokenizer's `batch_decode method`. Returns: `list[str]`: The decoded text. )skip_special_tokensclean_up_tokenization_spacesr)r8generated_outputsrrr9rrrpost_process_image_text_to_textsz.Glm4vProcessor.post_process_image_text_to_textcCs,|jj}|jj}tt||}|dgS)Nsecond_per_grid_ts)r(model_input_namesr'rdictfromkeys)r8tokenizer_input_namesimage_processor_input_namesnames_from_processorrrrrs z Glm4vProcessor.model_input_names)NNNN)NNN)NN)TF)rrr__doc__ attributesimage_processor_classvideo_processor_classtokenizer_classr4rrrr rrr r rrlrrrrpropertyr __classcell__rrr:rr&3s8 x& r&N)typingrrfeature_extraction_utilsr image_utilsrprocessing_utilsrrr r r r tokenization_utils_baser r video_utilsrrrr r&__all__rrrrs      u