qi;dZddlmZddlmZej eZGddeZGddeZ Gdd eZ d gZ y ) zMllama model configuration)PreTrainedConfig)loggingceZdZdZdZdZ ddedededed ed ed ed ed edededede edzde e edzdeffd Z e defdZ xZ S)MllamaVisionConfiga+ This is the configuration class to store the configuration of a [`MllamaVisionModel`]. It is used to instantiate an Mllama vision model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Mllama-11B. e.g. [meta-llama/Llama-3.2-11B-Vision](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision) Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PreTrainedConfig`] for more information. Args: hidden_size (`int`, *optional*, defaults to 1280): Dimensionality of the encoder layers and the pooler layer. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the Transformer encoder. num_global_layers (`int`, *optional*, defaults to 8): Number of global layers in the Transformer encoder. Vision model has a second transformer encoder, called global. num_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. num_channels (`int`, *optional*, defaults to 3): Number of channels in the input image. intermediate_size (`int`, *optional*, defaults to 5120): Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. vision_output_dim (`int`, *optional*, defaults to 7680): Dimensionality of the vision model output. Includes output of transformer encoder with intermediate layers and global transformer encoder. image_size (`int`, *optional*, defaults to 448): The size (resolution) of each image *tile*. patch_size (`int`, *optional*, defaults to 14): The size (resolution) of each patch. norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the layer normalization layers. max_num_tiles (`int`, *optional*, defaults to 4): Maximum number of tiles for image splitting. intermediate_layers_indices (`list[int]`, *optional*, defaults to [3, 7, 15, 23, 30]): Indices of intermediate layers of transformer encoder from which to extract and output features. These output features are concatenated with final hidden state of transformer encoder. supported_aspect_ratios (`list[list[int]]`, *optional*): List of supported aspect ratios for image splitting. If not specified, the default supported aspect ratios are [[1, 1], [1, 2], [1, 3], [1, 4], [2, 1], [2, 2], [3, 1], [4, 1]] for `max_num_tiles=4`. 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 MllamaVisionConfig, MllamaVisionModel >>> # Initializing a Llama config >>> config = MllamaVisionConfig() >>> # Initializing a vision model from the mllama-11b style configuration >>> model = MllamaVisionModel(config) >>> # Accessing the model configuration >>> configuration = model.config ```mllama_vision_model vision_configN hidden_size hidden_actnum_hidden_layersnum_global_layersnum_attention_heads num_channelsintermediate_sizevision_output_dim image_size patch_sizenorm_eps max_num_tilesintermediate_layers_indicessupported_aspect_ratiosinitializer_rangec Z|*| dk7r tdddgddgddgddgddgddgddgddgg}| gd} ||_||_||_||_||_| |_||_| |_| |_ ||_ | |_ | |_ ||_ ||_||_t!|Ddi|y)Nz;max_num_tiles must be 4 for default supported aspect ratiosr)r) ValueErrorr r r rrrrrrr rrattention_headsrrsuper__init__)selfr r r r r rrrrrrrrrrkwargs __class__s a/opt/pipecat/venv/lib/python3.12/site-packages/transformers/models/mllama/configuration_mllama.pyr$zMllamaVisionConfig.__init__Xs& # *! !^__()1v1v1v1v1vPQSTvXY[\W]`acd_e&f # & .*< '&$!2(!2$!2$+F(!2*  2'>$!2 "6"returnc,t|jS)N)lenr)r%s r(max_aspect_ratio_idz&MllamaVisionConfig.max_aspect_ratio_ids4//00r))igelu riiih㈵>rNN{Gz?)__name__ __module__ __qualname____doc__ model_typebase_config_keyintstrfloatlistr$propertyr- __classcell__r's@r(rrs< |'J%O  !#!"#%!%!%8<:>#'!*#*#*# *#  *# ! *#*#*#*#*#*#*#*#&*#Y%5*#"&d3i4!7*# !!*#X1S11r)rc&eZdZdZdZdZdZ ddededed ed ed ed ed e dzde dede de de de edzde dedededzf$fd Z xZS)MllamaTextConfigaV This is the configuration class to store the configuration of a [`MllamaTextModel`]. It is used to instantiate an Mllama text model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Mllama-11B. e.g. [meta-llama/Llama-3.2-11B-Vision](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision) 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 128256): Vocabulary size of the Mllama text model. Defines the maximum number of different tokens that can be represented by the `inputs_ids` passed when calling [`MllamaTextModel`]. hidden_size (`int`, *optional*, defaults to 4096): Dimensionality of the embeddings and hidden states. hidden_act (`str` or `Callable`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the encoder and pooler. 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 8): This is the number of key_value heads that should be used to implement Grouped Query Attention. If not specified, will default to `num_attention_heads`. intermediate_size (`int`, *optional*, defaults to 14336): Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. 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`. rms_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the rms normalization layers. max_position_embeddings (`int`, *optional*, defaults to 131072): 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. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie weight embeddings cross_attention_layers (`list[int]`, *optional*): Indices of the cross attention layers. If not specified, will default to [3, 8, 13, 18, 23, 28, 33, 38]. dropout (`float`, *optional*, defaults to 0): The dropout probability for self- and cross-attention layers. bos_token_id (`int`, *optional*, defaults to 128000): The id of the beginning of sentence token. eos_token_id (`int`, *optional*, defaults to 128001): The id of the end of sentence token. pad_token_id (`int`, *optional*, defaults to 128004): The id of the padding token. Example: ```python >>> from transformers import MllamaTextModel, MllamaTextConfig >>> # Initializing a Mllama text config >>> config = MllamaTextConfig() >>> # Initializing a model from the Mllama text configuration >>> model = MllamaTextModel(config) >>> # Accessing the model configuration >>> configuration = model.config ```mllama_text_model text_configgAN vocab_sizer r r r num_key_value_headsrrope_parameters rms_norm_epsmax_position_embeddingsr use_cachetie_word_embeddingscross_attention_layersdropout bos_token_id eos_token_id pad_token_idc ,|gd}||_||_||_||_||_||_| |_| |_| |_||_ ||_ ||_ | |_ ||_ | |_||_||_||_t%|Ldi|y)N)rr0 r!&r )rFr rMr r rGrrKrIrrNr rJrHrLrQrOrPr#r$)r%rFr r r r rGrrHrIrJrrKrLrMrNrOrPrQr&r's r(r$zMllamaTextConfig.__init__s, " )%C "$!2&<#&#6 #6 !2"(!2 $'>$.#6 ((( "6"r))isilu(r/r0i8Nr3ir4TFNiii)r5r6r7r8r9r: default_thetar;r<dictr=boolr>r$r@rAs@r(rCrCs A F%J#OM! !##%#$!''+"'.#'$)37""#)',#,#,# ,#  ,# ! ,#!,#,#,#,#"%,#!,#,#",#!%S D 0,# !,#"#,#$%,#&Dj',#,#r)rCc@eZdZdZdZddiZeedZ dfd Z xZ S) MllamaConfiga This is the configuration class to store the configuration of a [`MllamaForConditionalGeneration`]. It is used to instantiate an Mllama model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Mllama-9B. e.g. [meta-llama/Llama-3.2-11B-Vision](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision) Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PreTrainedConfig`] for more information. Args: vision_config (`Union[AutoConfig, dict]`, *optional*, defaults to `MllamaVisionConfig`): The config object or dictionary of the vision backbone. text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `MllamaTextConfig`): The config object or dictionary of the text backbone. image_token_index (`int`, *optional*, defaults to 128256): The image token index to encode the image prompt. Example: ```python >>> from transformers import MllamaForConditionalGeneration, MllamaConfig, MllamaVisionConfig, MllamaTextConfig >>> # Initializing a CLIP-vision config >>> vision_config = MllamaVisionConfig() >>> # Initializing a Llama config >>> text_config = MllamaTextConfig() >>> # Initializing a mllama-11b style configuration >>> configuration = MllamaConfig(vision_config, text_config) >>> # Initializing a model from the mllama-11b style configuration >>> model = MllamaForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```mllamaimage_token_idimage_token_index)rErc |%t|_tjdn8t |t rtdi||_nt |tr||_||_|%t|_tjdn8t |t rtdi||_nt |tr||_t|(di|y)Nz9vision_config is None, using default mllama vision configz5text_config is None, using default mllama text configr ) rrloggerinfo isinstancer]rcrCrEr#r$)r%rrErcr&r's r(r$zMllamaConfig.__init__.s  !3!5D  KKS T  t ,!3!Dm!DD   '9 :!.D !2  /1D  KKO P  T */>+>D   %5 6*D  "6"r))NNrX) r5r6r7r8r9 attribute_maprCr sub_configsr$r@rAs@r(r`r`s?% NJ-M#3EWXK ##r)r`N) r8configuration_utilsrutilsr get_loggerr5rerrCr`__all__r r)r(rns^!3   H %p1)p1ft#'t#nG##G#T  r)