% &hSddlmZddlmZejeZGddeZGddeZGddeZ gd Z y ) )PretrainedConfig)loggingceZdZdZddddddddZdZdZ dded ed ed ed ed edededede de ffd Z xZ S)Llama4VisionConfiga This is the configuration class to store the configuration of a [`Llama4VisionModel`]. It is used to instantiate a Llama4 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 Llama4 109B. e.g. [meta-llama/Llama-4-Scout-17B-16E](https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E) 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 768): 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 34): Number of hidden layers in the Transformer encoder. 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 5632): 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. vision_feature_layer (``, *optional*, defaults to -1): TODO vision_feature_select_strategy (`int`, *optional*, defaults to `"default"`): TODO initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. pixel_shuffle_ratio (`int`, *optional*, defaults to 0.5): TODO projector_input_dim (`int`, *optional*, defaults to 4096): TODO projector_output_dim (`int`, *optional*, defaults to 4096): TODO multi_modal_projector_bias (`int`, *optional*, defaults to `False`): TODO projector_dropout (`int`, *optional*, defaults to 0.0): TODO attention_dropout (`int`, *optional*, defaults to 0.0): TODO rope_theta (`int`, *optional*, defaults to 10000): TODO colwiserowwise colwise_rep)zmodel.layers.*.self_attn.q_projzmodel.layers.*.self_attn.k_projzmodel.layers.*.self_attn.v_projzmodel.layers.*.self_attn.o_projzvision_adapter.mlp.fc1zvision_adapter.mlp.fc2zpatch_embedding.linearllama4_vision_model vision_config hidden_size hidden_actnum_hidden_layersnum_attention_heads num_channelsintermediate_sizevision_output_dim image_size patch_sizenorm_epsinitializer_rangec <||_||_||_||_||_||_||_| |_| |_||_ | |_ ||_ ||_ ||_ ||_||_||_| |_| |_||_t)|Tdi|y)N)r r rrrrrrrrrpixel_shuffle_ratioprojector_input_dimprojector_output_dimmulti_modal_projector_biasprojector_dropoutattention_dropoutvision_feature_layervision_feature_select_strategy rope_thetasuper__init__)selfr r rrrrrrrrrr rrrrrrrr!kwargs __class__s /var/www/pru.catia.catastroantioquia-mas.com/valormas/lib/python3.12/site-packages/transformers/models/llama4/configuration_llama4.pyr#zLlama4VisionConfig.__init__Ts0'$!2(!2$!2$  #6 !2#6 #6 $8!*D'!2!2$8!.L+$ "6")igelu"riiih㈵>default{Gz?g?r1Fr2i') __name__ __module__ __qualname____doc__base_model_tp_plan model_typebase_config_keyintstrfloatr# __classcell__r&s@r'rrs,^,5+4+4+4"+"+"/'J%O !##%!%!%'0#' !#(+,#,#,# ,# ! ,#  ,#,#,#,#,#,#!,#,#r(rceZdZdZdZdgZidddddddd d d d d d d ddddddddddddddddddddZ dfd ZxZS)Llama4TextConfiga This is the configuration class to store the configuration of a [`Llama4TextModel`]. It is used to instantiate a Llama4 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 Llama4 109B. e.g. [meta-llama/Llama-4-Scout-17B-16E](https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E) 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 202048): Vocabulary size of the Llama4 text model. Defines the maximum number of different tokens that can be represented by the `inputs_ids` passed when calling [`Llama4TextModel`]. hidden_size (`int`, *optional*, defaults to 5120): Dimensionality of the embeddings and hidden states. intermediate_size (`int`, *optional*, defaults to 8192): Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. intermediate_size_mlp (`int`, *optional*, defaults to 16384): TODO num_hidden_layers (`int`, *optional*, defaults to 48): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 40): 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`. head_dim (`int`, *optional*, defaults to 128): TODO hidden_act (`str` or `Callable`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the encoder and pooler. 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. 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. pad_token_id (`int`, *optional*, defaults to 128004): The id of the padding token. bos_token_id (`int`, *optional*, defaults to 1): The id of the beginning of sentence token. eos_token_id (`int`, *optional*, defaults to 2): The id of the end of sentence token. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie weight embeddings rope_theta (`float`, *optional*, defaults to `500000.0`): The base period of the RoPE embeddings. attention_dropout (`int`, *optional*, defaults to 0.0): TODO num_experts_per_tok (`int`, *optional*, defaults to 1): TODO num_local_experts (`int`, *optional*, defaults to 16): TODO moe_layers (`int`, *optional*): TODO interleave_moe_layer_step (`int`, *optional*, defaults to 1): TODO use_qk_norm (`int`, *optional*, defaults to `True`): TODO output_router_logits (`int`, *optional*, defaults to `False`): TODO router_aux_loss_coef (`int`, *optional*, defaults to 0.001): TODO router_jitter_noise (`int`, *optional*, defaults to 0.0): TODO 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. `beta_fast` (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear ramp function. If unspecified, it defaults to 32. `beta_slow` (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear ramp function. If unspecified, it defaults to 1. `short_factor` (`List[float]`, *optional*): Only used with 'longrope'. The scaling factor to be applied to short contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 `long_factor` (`List[float]`, *optional*): Only used with 'longrope'. The scaling factor to be applied to long contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 `low_freq_factor` (`float`, *optional*): Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE `high_freq_factor` (`float`, *optional*): Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE no_rope_layers (`int`, *optional*): TODO no_rope_layer_interval (`int`, *optional*, defaults to 4): TODO attention_chunk_size (`int`, *optional*, defaults to 8192): attn_temperature_tuning (`int`, *optional*, defaults to 4): TODO floor_scale (`int`, *optional*, defaults to 8192): TODO attn_scale (`int`, *optional*, defaults to 0.1): TODO cache_implementation (``, *optional*, defaults to `"hybrid"`): Example: llama4_textpast_key_valueszlayers.*.self_attn.q_projrzlayers.*.self_attn.k_projzlayers.*.self_attn.v_projzlayers.*.self_attn.o_projrzlayers.*.input_layernorm.weightsequence_parallelz(layers.*.post_attention_layernorm.weightz norm.weightz-layers.*.feed_forward.shared_expert.gate_proj local_colwisez+layers.*.feed_forward.shared_expert.up_projz-layers.*.feed_forward.shared_expert.down_proj local_rowwisez*layers.*.feed_forward.experts.gate_up_projlocal_packed_rowwisez'layers.*.feed_forward.experts.down_projzlayers.*.feed_forward.expertslocalzlayers.*.feed_forward.gate_projzlayers.*.feed_forward.up_projzlayers.*.feed_forward.down_projzlayers.*.feed_forwardgatherc$ t'|d||||d|$| |_|"|_|!|_||_| |_||_||_||_ ||_ ||_ ||_ d|_ |#|_||}||_| |_| |_| |_| |_||_||_||n|j|jz|_||_||_||_||_||_||_t;|jD%cgc]}%t=|%dz|zdk7}&}%|r|n|&|_||_ ||ntCt;|dz |||_"||_#ycc}%w)N) pad_token_id bos_token_id eos_token_idtie_word_embeddingsFr)$r"r#attn_temperature_tuning attn_scale floor_scale vocab_sizemax_position_embeddingsr rintermediate_size_mlprr rope_scalingattention_biascache_implementationnum_key_value_headsr r rms_norm_eps use_cacher!rhead_dim use_qk_normnum_experts_per_toknum_local_expertsoutput_router_logitsrouter_aux_loss_coefrouter_jitter_noiseranger:no_rope_layersinterleave_moe_layer_steplist moe_layersattention_chunk_size)(r$rSr rrUrrrYr\r rTrrZr[rJrKrLrMr!rr^r_rgrer]r`rarbrVrdno_rope_layer_intervalrhrPrRrQrXr% layer_idxdefault_no_rope_layersr&s( r'r#zLlama4TextConfig.__init__sN  %%% 3    (?$$&$'>$&!2%:"!2#6 (#$8!  &"5 #6 $!2("$!2$,$8d>N>NRVRjRj>j &#6 !2$8!$8!#6 PUVZVlVlPm" CLCQ"88A= >" " 1?nDZ)B&% e59;LNghi  %9!" s E")#i@i i@0(siluir0r-TNrNFi r2rNr+NrNTFgMbP?r2NNrlrsrlg?hybrid) r3r4r5r6r8keys_to_ignore_at_inferencer7r#r=r>s@r'r@r@sfiVJ#4"5#Y#Y $Y $Y  *+>  34G  * 8 6 8 56L 2? ( *? ( *?!" #,# )!"#"" ! !%I\9\9r(r@cFeZdZdZdZeedZddiZ dfd Z xZ S) Llama4Configa This is the configuration class to store the configuration of a [`Llama4Model`]. It is used to instantiate an Llama4 model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Llama4 109B. e.g. [meta-llama/Llama-4-Scout-17B-16E](https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E) Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vision_config (`Llama4VisionConfig`, *optional*): The Llama4 Vision config. text_config (`Llama4TextConfig`, *optional*): The Llama4 Text config. boi_token_index (`int`, *optional*, defaults to 200080): The begin-of-image token index to wrap the image prompt. eoi_token_index (`int`, *optional*, defaults to 200081): The end-of-image token index to wrap the image prompt. image_token_index (`int`, *optional*, defaults to 200092): The image token index to encode the image prompt. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether the model's input and output word embeddings should be tied. ```python >>> from transformers import Llama4Model, Llama4Config >>> # Initializing a Llama4 7B style configuration >>> configuration = Llama4Config() >>> # Initializing a model from the Llama4 7B style configuration >>> model = Llama4Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```llama4) text_configr zmulti_modal_projector.linear_1r c |%t|_tjdn8t |t rtdi||_nt |tr||_||_||_||_|%t|_ tjdn8t |t rtdi||_ nt |tr||_ t|0dd|i|y)Nz9vision_config is None, using default llama4 vision configz5text_config is None, using default llama4 text configrMr) rr loggerinfo isinstancedictboi_token_indexeoi_token_indeximage_token_indexr@ryr"r#) r$r ryrrrrMr%r&s r'r#zLlama4Config.__init__s  !3!5D  KKS T  t ,!3!Dm!DD   '9 :!.D ..!2  /1D  KKO P  T */>+>D   %5 6*D  K-@KFKr()NNi i i F) r3r4r5r6r8r@r sub_configsr7r#r=r>s@r'rwrwdsH$ LJ"2EWXK(-  !LLr(rw)rwr@rN) configuration_utilsrutilsr get_loggerr3r{rr@rw__all__rr(r'rs[$4   H %g#)g#T^9'^9BJL#JLZ Er(