`iDzddlmZddlmZGddeZGddeZGddeZddgZd S) )PretrainedConfig)rope_config_validationcJeZdZdZdZdZ 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: hidden_size (`int`, *optional*, defaults to 1536): Dimensionality of the encoder layers and the pooler layer. depth (`int`, *optional*, defaults to 24): Number of layers (depth) in the model. attention_bias (`bool`, *optional*, defaults to `False`): Whether to add a bias to the queries, keys and values. intermediate_size (`int`, *optional*, defaults to 13696): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. hidden_act (`str` or `function`, *optional*, defaults to `"selu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. hidden_dropout_prob (`float`, *optional*, defaults to 0.0): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.0): Dropout probability for attention weights. projection_dropout (`float`, *optional*, defaults to 0.0): Dropout probability for the projection layer. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. image_size (`int` or `list[int]`, *optional*, defaults to `[336, 336]`): The size (resolution) of each image. patch_size (`int`, *optional*, defaults to `14`): The size (resolution) of each patch. num_channels (`int`, *optional*, defaults to 3): The number of input channels. out_hidden_size (`int`, *optional*, defaults to 4096): The output hidden size of the vision model. 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. 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_configsiluF rPh㈵>5{Gz?c tjdi|||_||_||_||_||_||_| |_| |_ | |_ | |_ ||_ ||_ | |_||_||_dS)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)selfrrrr&r'rrrrr%r r!r"r#r$kwargs __class__s /home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/transformers/models/glm4v/configuration_glm4v.pyrzGlm4vVisionConfig.__init__Ts& ""6""" &$"&$$"4#6 .!2!2(,!2)r r r Fr r rrrrrrrrr)__name__ __module__ __qualname____doc__ model_typebase_config_keyr __classcell__r*s@r+rrs5 5 nJ%O!#3#3#3#3#3#3#3#3#3#3r,rceZdZdZdZdZdgZddddddd Zd gd gfd d gd gfd gd gfdZ dfd Z xZ S) 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`. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether the model's input and output word embeddings should be tied. rope_theta (`float`, *optional*, defaults to 10000.0): The base period of the RoPE embeddings. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. rope_scaling (`Dict`, *optional*): Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value accordingly. Expected contents: `rope_type` (`str`): The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope', 'llama3'], with 'default' being the original RoPE implementation. `factor` (`float`, *optional*): Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In most scaling types, a `factor` of x will enable the model to handle sequences of length x * original maximum pre-trained length. `original_max_position_embeddings` (`int`, *optional*): Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during pretraining. `attention_factor` (`float`, *optional*): Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention computation. If unspecified, it defaults to value recommended by the implementation, using the `factor` field to infer the suggested value. image_token_id (`int`, *optional*): Token index used as placeholder for image embeddings. video_token_id (`int`, *optional*): Token index used as placeholder for video embeddings. ```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_valuescolwiserowwise colwise_rep rowwise_rep)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_tokenslayersnormPrr( rr rrTF@r Nc ||_||_||_||_||_||_||}||_||_| |_| |_ | |_ | |_ ||_ ||_ |j d|j vr|j d|j d<t|dh||_||_t#jdd| i|dS)Ntype rope_type mrope_section) ignore_keystie_word_embeddingsr) vocab_sizemax_position_embeddingsrr#num_hidden_layersnum_attention_headsnum_key_value_headsrr$r% use_cache rope_thetar' rope_scalingrimage_token_idvideo_token_idrr)r(rPrr#rRrSrTrrQr$r%rUrOrVr'rWrXrYr)r*s r+rzGlm4vTextConfig.__init__s*%'>$&!2!2#6   &"5 #6 $!2("$!2(   (Vt7H-H-H-1->v-FD k *t/1BCCCC,,KK-@KFKKKKKr,)rErrrFrGrr rHrrTFrIr NNN) r-r.r/r0r1r2keys_to_ignore_at_inferencebase_model_tp_planbase_model_pp_planrr3r4s@r+r6r6zsP P dJ#O#4"5&/%.%.%.%2"/ &(9:#%568IJ!"_$56 %!%1L1L1L1L1L1L1L1L1L1Lr,r6cHeZdZdZdZeedZdgZ d fd 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. ```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 ```r)rr8r9N/O0O+O,O-O.Oc  t|tr|jddi||_n||jd|_t|tr|jddi||_n||jddi| |_||_||_||_||_||_ ||_ tj di| dS)Nrr8r) isinstancedict sub_configsrr8rXrYvideo_start_token_idvideo_end_token_idimage_start_token_idimage_end_token_idrr) r(r8rrXrYrkrlrirjr)r*s r+rzGlm4vConfig.__init__As mT * * E!B!1/!B!S!S]!S!SD    "!B!1/!B!D!DD  k4 ( ( I>t/ >MMMMD    >t/ >HHHHD ,,$8!"4$8!"4""6"""""r,)NNr_r`rarbrcrd) r-r.r/r0r1rr6rhrZrr3r4s@r+r^r^s' ' RJ$5oVVK#4"5#!#!##########r,r^N)configuration_utilsrmodeling_rope_utilsrrr6r^__all__rr,r+rps*433333999999^3^3^3^3^3(^3^3^3BVLVLVLVLVL&VLVLVLrK#K#K#K#K#"K#K#K#\ + ,r,