`i)@zddlmZddlmZddlmZddlmZmZGddeZ Gd d eZ d dgZ d S) )Optional)PretrainedConfig)rope_config_validation)CONFIG_MAPPING AutoConfigc eZdZdZdZdgZddddddddZdgdgfd d gd gfd gd gfd Zd Z d!de de de de ffd Z xZ S)"AriaTextConfigaD This class handles the configuration for the text component of the Aria model. Instantiating a configuration with the defaults will yield a similar configuration to that of the model of the Aria [rhymes-ai/Aria](https://huggingface.co/rhymes-ai/Aria) architecture. This class extends the LlamaConfig to include additional parameters specific to the Mixture of Experts (MoE) architecture. Args: vocab_size (`int`, *optional*, defaults to 32000): Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`LlamaModel`] hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 4096): The size of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer in the Transformer decoder. num_key_value_heads (`int`, *optional*): 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, check out [this paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `num_attention_heads`. 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 2048): The maximum sequence length that this model might ever be used with. Llama 1 supports up to 2048 tokens, Llama 2 up to 4096, CodeLlama up to 16384. 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-06): 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`. pad_token_id (`int`, *optional*, defaults to 2): Padding token id. bos_token_id (`int`, *optional*, defaults to 1): Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 2): End of stream token id. pretraining_tp (`int`, *optional*, defaults to 1): Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to understand more about it. This value is necessary to ensure exact reproducibility of the pretraining results. Please refer to [this issue](https://github.com/pytorch/pytorch/issues/76232). tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie weight embeddings rope_theta (`float`, *optional*, defaults to 10000.0): The base period of the RoPE embeddings. 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 attention_bias (`bool`, *optional*, defaults to `False`): Whether to use a bias in the query, key, value and output projection layers during self-attention. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. mlp_bias (`bool`, *optional*, defaults to `False`): Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers. head_dim (`int`, *optional*): The attention head dimension. If None, it will default to hidden_size // num_heads moe_num_experts (`int`, *optional*, defaults to 8): The number of experts in the MoE layer. moe_topk (`int`, *optional*, defaults to 2): The number of top experts to route to for each token. moe_num_shared_experts (`int`, *optional*, defaults to 2): The number of shared experts. aria_textpast_key_valuescolwiserowwise)zlayers.*.self_attn.q_projzlayers.*.self_attn.k_projzlayers.*.self_attn.v_projzlayers.*.self_attn.o_projzlayers.*.mlp.gate_projzlayers.*.mlp.up_projzlayers.*.mlp.down_proj input_ids inputs_embeds hidden_statesattention_mask) embed_tokenslayersnorm text_config} Nsilu{Gz?ư>TrF@intermediate_sizemoe_num_expertsmoe_topkmoe_num_shared_expertsc tjd| | ||d|||_||_||_||_||_||_||}||_||_ | |_ | |_ ||_ | |_ ||_||_||_||_||_||n|j|jz|_|jd|jvr|jd|jd<t)|||_||_||_dS)N) pad_token_id bos_token_id eos_token_idtie_word_embeddingstype rope_type)super__init__ vocab_sizemax_position_embeddings hidden_sizer#num_hidden_layersnum_attention_headsnum_key_value_heads hidden_actinitializer_range rms_norm_epspretraining_tp use_cache rope_theta rope_scalingattention_biasattention_dropoutmlp_biashead_dimrr$r%r&)selfr1r3r#r4r5r6r7r2r8r9r;r(r)r*r:r+r<r=r>r?r@rAr$r%r&kwargs __class__s /home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/transformers/models/aria/configuration_aria.pyr0zAriaTextConfig.__init__sG:  %%% 3       %'>$&!2!2#6   &"5 #6 $!2(,"$(,!2  $,$8d>NRVRj>j    (Vt7H-H-H-1->v-FD k *t$$$.  &<###)rrrrrNrrrrTrrrrFr NFr!FNr"rr) __name__ __module__ __qualname____doc__ model_typekeys_to_ignore_at_inferencebase_model_tp_planbase_model_pp_planbase_config_keyintr0 __classcell__rDs@rEr r sIhhTJ#4"5&/%.%.%."+ )"+&(9:#%568IJ!"_$56 $O!%  $! &'5B=B= B=01B=23B=4!$5B=B=B=B=B=B=B=B=B=B=rFr c heZdZdZdZddiZeedZ dd e d ed e e de d e f fd Z xZS) AriaConfiga This class handles the configuration for both vision and text components of the Aria model, as well as additional parameters for image token handling and projector mapping. Instantiating a configuration with the defaults will yield a similar configuration to that of the model of the Aria [rhymes-ai/Aria](https://huggingface.co/rhymes-ai/Aria) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vision_config (`AriaVisionConfig` or `dict`, *optional*): Configuration for the vision component. vision_feature_layer (`int`, *optional*, defaults to -1): The index of the layer to select the vision feature. text_config (`AriaTextConfig` or `dict`, *optional*): Configuration for the text component. projector_patch_to_query_dict (`dict`, *optional*): Mapping of patch sizes to query dimensions. image_token_index (`int`, *optional*, defaults to 9): Index used to represent image tokens. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated normal initializer for initializing all weight matrices. Attributes: model_type (`str`): Type of the model, set to `"aria"`. image_token_index (`int`): Index used to represent image tokens. projector_patch_to_query_dict (`dict`): Mapping of patch sizes to query dimensions. vision_config (`AriaVisionConfig`): Configuration for the vision component. text_config (`AriaTextConfig`): Configuration for the text component. ariaimage_token_idimage_token_index)r vision_configN rvision_feature_layerrprojector_patch_to_query_dictr8c (||_|ddd}d|D|_t|j|_||_t|trd|d<t|ddi|}n|td}||_ ||_ t|trd|vr tdi|}n|t}||_ tjdi|dS)N)ii$cNi|]"\}}t|t|#Sr.)rP).0kvs rE z'AriaConfig.__init__..s*-o-o-oAc!ffc!ff-o-o-orFidefics3_visionrKr.)rWitemsr\maxvalues'max_value_projector_patch_to_query_dictr[ isinstancedictrrXr8r rr/r0) rBrXr[rr\rWr8rCrDs rEr0zAriaConfig.__init__ sE"3 ) 0-- ).p-oIfIlIlInIn-o-o-o*7:4;];d;d;f;f7g7g4$8! mT * * @*;M, '*=+FGXX-XXMM  "*+<=??M*!2 k4 ( ( +\[-H-H(77;77KK  (**K&""6"""""rF)NrYNNrZr)rGrHrIrJrK attribute_mapr r sub_configsrPrrkfloatr0rQrRs@rErTrTs""HJ-M#1:NNK$&&*8rts*333333999999--------@=@=@=@=@=%@=@=@=FQ#Q#Q#Q#Q#!Q#Q#Q#h ) *rF