sg&fdZddlmZddlmZddlmZejeZ GddeZ y) zFuyu model configuration)PretrainedConfig)logging)CONFIG_MAPPINGcdeZdZdZdZdgZ dfd ZdZxZS) FuyuConfiga This is the configuration class to store the configuration of a [`FuyuForCausalLM`]. It is used to instantiate an Fuyu 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 [adept/fuyu-8b](https://huggingface.co/adept/fuyu-8b). 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 262144): Vocabulary size of the Fuyu model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`FuyuForCausalLM`] hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 16384): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 36): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 64): Number of attention heads for each attention layer in the Transformer encoder. hidden_act (`str` or `function`, *optional*, defaults to `"relu2"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to 16384): The maximum sequence length that this model might ever be used with. image_size (`int`, *optional*, defaults to 300): The input image size. patch_size (`int`, *optional*, defaults to 30): The input vision transformer encoding patch size. num_channels (`int`, *optional*, defaults to 3): The input image number of channels. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_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`. Whether to tie weight embeddings tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie input and output embeddings. rope_theta (`float`, *optional*, defaults to 25000.0): The base period of the RoPE embeddings. rope_scaling (`Dict`, *optional*): Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update `max_position_embeddings` to the expected new maximum. See the following thread for more information on how these scaling strategies behave: https://www.reddit.com/r/LocalFuyu/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an experimental feature, subject to breaking API changes in future versions. qk_layernorm (`bool`, *optional*, defaults to `True`): Whether or not to normalize the Queries and Keys after projecting the hidden states hidden_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio after applying the MLP to the hidden states. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio after computing the attention scores. partial_rotary_factor (`float`, *optional*, defaults to 0.5): Percentage of the query and keys which will have rotary embedding. pad_token_id (`int`, *optional*): The id of the *padding* token. bos_token_id (`int`, *optional*, defaults to 1): The id of the *beginning-of-sequence* token. eos_token_id (`Union[int, List[int]]`, *optional*, defaults to 2): The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens. text_config (`dict`, *optional*): Dictionary of configuration options used to initialize the `language``[`Aut`]. ```python >>> from transformers import FuyuConfig >>> # Initializing a Fuyu fuyu-7b style configuration >>> configuration = FuyuConfig() ```fuyupast_key_valuesc 8|Pid|d|d|d|d|d|d|d| d | d | d |d |d |d|d|d|d||||d}tjdd|vr|dnd}t|di||_||_||_||_| |_| |_||_ ||_ ||_ ||_ ||_ | |_| |_| |_||_||_||_||_||_||_|j/t1|dd||||d|y)N vocab_sizemax_position_embeddings hidden_sizeintermediate_sizenum_hidden_layersnum_attention_heads hidden_actinitializer_rangelayer_norm_eps use_cache rope_theta rope_scaling qk_layernormhidden_dropoutattention_dropoutpartial_rotary_factor pad_token_id) bos_token_id eos_token_idtie_word_embeddingszEtext_config is None. initializing the text model with default values. model_type persimmon)rrrr)loggerinfor text_config _vocab_sizer image_size patch_size num_channelsrrrrrrrrrrrrrr_rope_scaling_validationsuper__init__)selfr rrrrrr r'r(r)rrrrrrrrrrrrrr%kwargstext_model_type __class__s ^/var/www/html/venv/lib/python3.12/site-packages/transformers/models/fuyu/configuration_fuyu.pyr,zFuyuConfig.__init__is8  j)+B{$%6  $%6  &': j$%6!.Yj  !.$%6 ()>!" #$!- ,':)K, KK_ `7C{7R+l3Xc)/:I[I%'>$$$(&!2!2#6 $!2,"$((,!2%:" %%'  %%% 3    c|jyt|jtrt|jdk7rt d|j|jj dd}|jj dd}||dvrt d||t|t r|dkrt d |y) z< Validate the `rope_scaling` configuration. NrzN`rope_scaling` must be a dictionary with two fields, `type` and `factor`, got typefactor)lineardynamiczF`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got g?z7`rope_scaling`'s factor field must be a float > 1, got )r isinstancedictlen ValueErrorgetfloat)r-rope_scaling_typerope_scaling_factors r1r*z#FuyuConfig._rope_scaling_validations    $ $++T2c$:K:K6LPQ6Qcdhduducvw !--11&$?"//33HdC  $(9AV(VXYjXkl   &j9Le.TXkorXrVWjVklm mYsr2)ii@$@relu2r@i,rg{Gz?gh㈵>TFgj@NTrEg?NrN) __name__ __module__ __qualname____doc__r keys_to_ignore_at_inferencer,r* __classcell__)r0s@r1rrsqJ XJ#4"5 %!!3R hnr2rN) rJconfiguration_utilsrutilsrautor get_loggerrGr#rr"r2r1rQs83!   H %vn!vnr2