qi%>fddlmZddlmZGddeZGddeZGddeZgd Zy ) )PreTrainedConfig)RopeParameterscJeZdZdZdZdZ dfd ZxZS)Glm4vVisionConfiga This is the configuration class to store the configuration of a [`Glm4vVisionModel`]. It is used to instantiate an Glm4vVisionModel model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of GLM-4.1V-9B-Thinking [THUDM/GLM-4.1V-9B-Thinking](https://huggingface.co/THUDM/GLM-4.1V-9B-Thinking). Args: depth (`int`, *optional*, defaults to 24): Number of layers (depth) in the model. hidden_size (`int`, *optional*, defaults to 1536): Dimensionality of the encoder layers and the pooler layer. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. attention_bias (`bool`, *optional*, defaults to `False`): Whether to add a bias to the queries, keys and values. attention_dropout (`float`, *optional*, defaults to 0.0): Dropout probability for attention weights. num_heads (``, *optional*, defaults to 12): in_channels (``, *optional*, defaults to 3): image_size (`int` or `list[int]`, *optional*, defaults to 336): The size (resolution) of each image. patch_size (`int`, *optional*, defaults to 14): The size (resolution) of each patch. rms_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the rms normalization layers. spatial_merge_size (`int`, *optional*, defaults to 2): The size used for merging spatial dimensions. temporal_patch_size (`int`, *optional*, defaults to 2): The size used for patches along the temporal dimension. out_hidden_size (`int`, *optional*, defaults to 4096): The output hidden size of the vision model. intermediate_size (`int`, *optional*, defaults to 13696): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. Example: ```python >>> from transformers import Glm4vVisionConfig, Glm4vVisionModel >>> # Initializing a Glm4vVisionConfig GLM-4.1V-9B style configuration >>> configuration = Glm4vVisionConfig() >>> # Initializing a model (with random weights) from the GLM-4.1V-9B configuration >>> model = Glm4vVisionModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` glm4v_vision vision_configc t|di|||_||_||_||_||_||_| |_| |_ | |_ | |_ ||_ ||_ | |_||_||_y)N)super__init__depth hidden_size hidden_act num_heads in_channels image_size patch_sizespatial_merge_sizetemporal_patch_sizeout_hidden_sizeintermediate_sizeinitializer_range rms_norm_epsattention_biasattention_dropout)selfr rrrrrrrrrrrrrrkwargs __class__s _/opt/pipecat/venv/lib/python3.12/site-packages/transformers/models/glm4v/configuration_glm4v.pyr zGlm4vVisionConfig.__init__Os& "6" &$"&$$"4#6 .!2!2(,!2)isiluF riPh㈵>r'5{Gz?)__name__ __module__ __qualname____doc__ model_typebase_config_keyr __classcell__rs@rrrsN1 f J%O!#3#3r rc$eZdZdZdZdZdgZddddddd Zd gd gfd d gd gfd gd gfdZ dde dzde dzde dzde dzde dzde dzde dzde dzde dzde dzde dzde dzde ee e fzdzde dzffd ZxZS) Glm4vTextConfiga This is the configuration class to store the configuration of a [`Glm4vModel`]. It is used to instantiate a GLM-4.1V model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of GLM-4.1V-9B-Thinking [THUDM/GLM-4.1V-9B-Thinking](https://huggingface.co/THUDM/GLM-4.1V-9B-Thinking). Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PreTrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 151552): Vocabulary size of the Glm4v model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Glm4vModel`] hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 13696): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 40): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer in the Transformer encoder. num_key_value_heads (`int`, *optional*, defaults to 2): This is the number of key_value heads that should be used to implement Grouped Query Attention. If `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details checkout [this paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `32`. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to 32768): The maximum sequence length that this model might ever be used with. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. rms_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the rms normalization layers. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. rope_parameters (`RopeParameters`, *optional*): Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE with longer `max_position_embeddings`. pad_token_id (`int`, *optional*): The id of the padding token. ```python >>> from transformers import Glm4vTextModel, Glm4vConfig >>> # Initializing a GLM-4.1V style configuration >>> configuration = Glm4vConfig() >>> # Initializing a model from the GLM-4.1V style configuration >>> model = Glm4vTextModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` glm4v_text text_configpast_key_valuescolwiserowwisecolwise_gather_outputrowwise_split_input)zlayers.*.self_attn.q_projzlayers.*.self_attn.k_projzlayers.*.self_attn.v_projzlayers.*.self_attn.o_projzlayers.*.mlp.gate_up_projzlayers.*.mlp.down_proj input_ids inputs_embeds hidden_statesattention_mask) embed_tokenslayersnormN vocab_sizerrnum_hidden_layersnum_attention_headsnum_key_value_headsrmax_position_embeddingsrr use_cacherrope_parameters pad_token_idc ||_||_||_||_||_||_||}||_||_| |_| |_ | |_ | |_ | |_ ||_ t|<dddhi|y)Nignore_keys_at_rope_validation mrope_sectionr )rCrGrrrDrErFrrrrHrrIrJr r )rrCrrrDrErFrrGrrrHrrIrJrrs rr zGlm4vTextConfig.__init__s$%'>$&!2!2#6   &"5 #6 $!2("!2.( T8ITVTr )iPr(r)( r'r"ir*r&Tr#NN)r+r,r-r.r/r0keys_to_ignore_at_inferencebase_model_tp_planbase_model_pp_planintstrfloatboolrdictr r1r2s@rr4r4us< |J#O#4"5&/%.%.%.%<"7 &(9:#%568IJ!"_$56"("&(-(**,*+!'.3*.#(!%*-MQ#'&U$J&U4Z&U: &U : &U !4Z &U!4Z&U$J&U"%t&U!4<&UDj&U$;&U!4<&U($sN/B*CCdJ&UDj&U&Ur r4cJeZdZdZdZeedZdgZ dfd Z xZ S) Glm4vConfiga This is the configuration class to store the configuration of a [`Glm4vModel`]. It is used to instantiate a GLM-4.1V model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of GLM-4.1V-9B-Thinking [THUDM/GLM-4.1V-9B-Thinking](https://huggingface.co/THUDM/GLM-4.1V-9B-Thinking). Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PreTrainedConfig`] for more information. Args: text_config (`Union[PreTrainedConfig, dict]`, *optional*, defaults to `Glm4vTextConfig`): The config object or dictionary of the text backbone. vision_config (`Union[PreTrainedConfig, dict]`, *optional*, defaults to `Glm4vVisionConfig`): The config object or dictionary of the vision backbone. image_token_id (`int`, *optional*, defaults to 151343): The image token index to encode the image prompt. video_token_id (`int`, *optional*, defaults to 151344): The video token index to encode the image prompt. image_start_token_id (`int`, *optional*, defaults to 151339): The image start token index to encode the start of image. image_end_token_id (`int`, *optional*, defaults to 151340): The image end token index to encode the end of image. video_start_token_id (`int`, *optional*, defaults to 151341): The video start token index to encode the start of video. video_end_token_id (`int`, *optional*, defaults to 151342): The video end token index to encode the end of video. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether the model's input and output word embeddings should be tied. ```python >>> from transformers import Glm4vForConditionalGeneration, Glm4vConfig >>> # Initializing a GLM-4.1V style configuration >>> configuration = Glm4vConfig() >>> # Initializing a model from the GLM-4.1V style configuration >>> model = Glm4vForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```glm4v)rr6r7c  t|tr|jddi||_n||jd|_t|tr|jddi||_n||jddi| |_||_||_||_||_||_ ||_ | |_ t |4di| y)Nrr6r ) isinstancerW sub_configsrr6image_token_idvideo_token_idvideo_start_token_idvideo_end_token_idimage_start_token_idimage_end_token_idtie_word_embeddingsr r ) rr6rr^r_rbrcr`rardrrs rr zGlm4vConfig.__init__s mT *!B!1!1/!B!S]!SD   "!B!1!1/!B!DD  k4 (>t// >MMD   >t// >HHD ,,$8!"4$8!"4#6  "6"r ) NNi/Oi0Oi+Oi,Oi-Oi.OF) r+r,r-r.r/rr4r]rPr r1r2s@rrYrYsJ) VJ$5oVK#4"5#!#!!##r rY)rYr4rN)configuration_utilsrmodeling_rope_utilsrrr4rY__all__r r rrhsG(41Z3(Z3zwU&wUtO#"O#d Br