S^(h?lddlmZddlmZddlmZddlmZmZGddeZ Gd d eZ d dgZ y ) )Dict)PretrainedConfig)rope_config_validation)CONFIG_MAPPING AutoConfigc eZdZdZdZdgZddddddddZdgdgfd d gd gfd gd gfd Zd Z dd e de de de ffd Z xZ S)AriaTextConfiga> 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 checkout [this paper](https://arxiv.org/pdf/2305.13245.pdf). 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_configintermediate_sizemoe_num_expertsmoe_topkmoe_num_shared_expertsc t|d| | ||d|||_||_||_||_||_||_||}||_||_ | |_ | |_ ||_ | |_ ||_||_||_||_||_||n|j|jz|_|j*d|jvr|jd|jd<t)|||_||_||_y)N) pad_token_id bos_token_id eos_token_idtie_word_embeddingstype rope_type)super__init__ vocab_sizemax_position_embeddings hidden_sizernum_hidden_layersnum_attention_headsnum_key_value_heads hidden_actinitializer_range rms_norm_epspretraining_tp use_cache rope_theta rope_scalingattention_biasattention_dropoutmlp_biashead_dimrrrr)selfr&r(rr)r*r+r,r'r-r.r0rrrr/r r1r2r3r4r5r6rrrkwargs __class__s i/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/transformers/models/aria/configuration_aria.pyr%zAriaTextConfig.__init__s5:  %%% 3    %'>$&!2!2#6   &"5 #6 $!2(,"$(,!2  $,$8d>N>NRVRjRj>j     (Vt7H7H-H-1->->v-FD  k *t$.  &<#)i}r< r=Nsilui{Gz?gư>Trrr@Fg@NFgFNrr) __name__ __module__ __qualname____doc__ model_typekeys_to_ignore_at_inferencebase_model_tp_planbase_model_pp_planbase_config_keyintr% __classcell__r9s@r:r r shTJ#4"5&/%.%.%."+ )"+&(9:#%568IJ!"_$56 $O!%  $! &'5B= B=01B=23B=4!$5B=B=r;r c TeZdZdZdZeedZ d dedede dede f fd Z xZ S) AriaConfiga This class handles the configuration for both vision and text components of the Aria model, as well as additional parameters for image token handling and projector mapping. Instantiating a configuration with the defaults will yield a similar configuration to that of the model of the Aria [rhymes-ai/Aria](https://huggingface.co/rhymes-ai/Aria) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vision_config (`AriaVisionConfig` or `dict`, *optional*): Configuration for the vision component. vision_feature_layer (`int`, *optional*, defaults to -1): The index of the layer to select the vision feature. text_config (`AriaTextConfig` or `dict`, *optional*): Configuration for the text component. projector_patch_to_query_dict (`dict`, *optional*): Mapping of patch sizes to query dimensions. image_token_index (`int`, *optional*, defaults to 9): Index used to represent image tokens. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated normal initializer for initializing all weight matrices. Attributes: model_type (`str`): Type of the model, set to `"aria"`. image_token_index (`int`): Index used to represent image tokens. projector_patch_to_query_dict (`dict`): Mapping of patch sizes to query dimensions. vision_config (`AriaVisionConfig`): Configuration for the vision component. text_config (`AriaTextConfig`): Configuration for the text component. aria)r vision_configvision_feature_layerrprojector_patch_to_query_dictimage_token_indexr-c ||_|ddd}|jD cic]\}} t|t| c} }|_t |jj |_||_t|trd|d<t|ddi|}n| td}||_ ||_ t|trd|vr tdi|}n | t}||_t |@di|ycc} }w)N)ii$idefics3_visionrFr#)rTitemsrKrSmaxvalues'max_value_projector_patch_to_query_dictrR isinstancedictrrQr-r rr$r%) r7rQrRrrSrTr-r8kvr9s r:r%zAriaConfig.__init__s"3 ) 0- )JgIlIlIn-oAc!fc!fn-o*7:4;];];d;d;f7g4$8! mT **;M, '*=+FGX-XM  "*+<=?M*!2 k4 (\[-H(7;7K  (*K& "6"'.psD)NNN r?) rBrCrDrErFr r sub_configsrKrfloatr%rLrMs@r:rOrOsj"HJ"0:NK$&&*.2!"#'&#"&#$ &# (, &#  &#!&#&#r;rON) typingrconfiguration_utilsrmodeling_rope_utilsrautorr r rO__all__r#r;r:rjs@*39-@=%@=FN#!N#b ) *r;