g]3dZddlmZddlmZddlmZejeZ GddeZ Gdd eZ d S) zchameleon model configuration)List)PretrainedConfig)loggingceZdZdZdZdddddddgd d d d d df dedededededededeededeedede ffd Z xZ S)ChameleonVQVAEConfiga This is the configuration class to store the configuration of a [`ChameleonVQModel`]. It is used to instantiate a `ChameleonVQModel` according to the specified arguments, defining the model architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Instantiating a configuration with the defaults will yield a similar configuration to the VQModel of the [meta/chameleon-7B](https://huggingface.co/meta/chameleon-7B). Args: embed_dim (`int`, *optional*, defaults to 256): Dimensionality of each embedding vector. num_embeddings (`int`, *optional*, defaults to 8192): Number of codebook embeddings. double_latent (`bool`, *optional*, defaults to `False`): Whether to use double z channels. latent_channels (`int`, *optional*, defaults to 256): Number of channels for the latent space. resolution (`int`, *optional*, defaults to 512): Resolution of the input images. in_channels (`int`, *optional*, defaults to 3): Number of input channels. base_channels (`int`, *optional*, defaults to 128): Base channel count. channel_multiplier (`List[int]`, *optional*, defaults to `[1, 1, 2, 2, 4]`): Channel multipliers for each resolution. num_res_blocks (`int`, *optional*, defaults to 2): Number of residual blocks. attn_resolutions (`List[int]`, *optional*): Resolutions to apply attention. dropout (`float`, *optional*, defaults to 0.0): Dropout rate. attn_type (`str`, *optional*, defaults to `"vanilla"`): Attention type used in VQ-GAN encoder. Can be "vanilla" or None. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. chameleon_vqgani Fir)r r r Nvanilla{Gz? embed_dimnum_embeddings double_latentlatent_channels resolution in_channels base_channelschannel_multipliernum_res_blocksattn_resolutionsdropout attn_typec tjdi|||_||_||_||_||_||_||_||_ | |_ | |_ | |_ | |_ | |_dS)N)super__init__rrrrrrrrrrrrinitializer_range)selfrrrrrrrrrrrrr"kwargs __class__s q/var/www/html/ai-engine/env/lib/python3.11/site-packages/transformers/models/chameleon/configuration_chameleon.pyr!zChameleonVQVAEConfig.__init__Bs" ""6"""",*.$&*"4, 0 "!2) __name__ __module__ __qualname____doc__ model_typeintboolrfloatstrr! __classcell__r%s@r&rrs##J#J"#" (7&*"3333 3  3  333!I33s)333333333333r'rcdeZdZdZdZdgZ dfd ZdZxZS)ChameleonConfiga This is the configuration class to store the configuration of a [`ChameleonModel`]. It is used to instantiate a chameleon 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 [meta/chameleon-7B](https://huggingface.co/meta/chameleon-7B). 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 65536): Vocabulary size of the chameleon model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`ChameleonModel`]; this includes text and image tokens. hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 11008): Dimension 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*, defaults to 32): 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 4096): The maximum sequence length that this model might ever be used with. Chameleon supports up to 4096 tokens. 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 (not used by all models). Only relevant if `config.is_decoder=True`. pad_token_id (`int`, *optional*): 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. 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. 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/Localchameleon/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an experimental feature, subject to breaking API changes in future versions. attention_bias (`bool`, defaults to `False`, *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. model_parallel_size (`int`, *optional*, defaults to 1): Number of shards used when training the model. This will be used in qk layernorm because the original Chameleon inference doesn't do reduction in those layers and each rank has its own biases. swin_norm (`bool`, *optional*, defaults to `False`): Use Swin Transformer normalization. vq_config (`dict`, *optional*): ChameleonVQConfig instance containing the configuration for the VQ-VAE model. vocabulary_map (`dict`, *optional*): A dictionary containing the vocabulary map from the tokenizer. Used to obtain tokens from the image inputs. 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. ```python >>> from transformers import ChameleonModel, ChameleonConfig >>> # Initializing a chameleon chameleon-7b style configuration >>> configuration = ChameleonConfig() >>> # Initializing a model from the chameleon-7b style configuration >>> model = ChameleonModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` chameleonpast_key_values+ silurh㈵>TNr r F@rc ||_||_||_||_||_||_||_||_||_| |_ | |_ | |_ ||_ ||_ |||_||_||_||_|i}t&dt+di||_||_t1jd| | ||d|dS)NzJvq_config is None. initializing the ChameleonVQConfig with default values.) pad_token_id bos_token_id eos_token_idtie_word_embeddingsr) vocab_sizemax_position_embeddings hidden_sizeintermediate_sizenum_hidden_layersnum_attention_headsmlp_biasnum_key_value_heads hidden_actr" rms_norm_eps use_cache rope_theta rope_scaling_rope_scaling_validationattention_biasattention_dropoutmodel_parallel_size swin_normloggerinfor vq_configvocabulary_mapr r!)r#rCrErFrGrHrJrKrDr"rLrMr?r@rArBrNrOrQrRrSrTrWrXrIr$r%s r&r!zChameleonConfig.__init__s!8%'>$&!2!2#6   #6 $!2("$( %%''',!2#6 "  I KKd e e e-:: ::, %%% 3         r'c|jdSt|jtrt|jdkrt d|j|jdd}|jdd}||dvrt d||t|t r|dkrt d |dS) z< Validate the `rope_scaling` configuration. Nr zS`rope_scaling` must be a dictionary with 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 )rO isinstancedictlen ValueErrorgetr/)r#rope_scaling_typerope_scaling_factors r&rPz(ChameleonConfig._rope_scaling_validations   $ F$+T22 c$:K6L6LPQ6Q6Q+(++ !-11&$??"/33HdCC  $(9AV(V(VlYjll   &j9Le.T.T &XkorXrXrlWjllmm mYsXrr')r7r8r9r:r:r:r;r8rr<TNr r Fr=NFrr FNNF) r(r)r*r+r,keys_to_ignore_at_inferencer!rPr1r2s@r&r4r4csW W rJ#4"5 $!3? ? ? ? ? ? Bnnnnnnnr'r4N) r+typingrconfiguration_utilsrutilsr get_loggerr(rUrr4rr'r&rjs$#333333  H % %F3F3F3F3F3+F3F3F3Rqnqnqnqnqn&qnqnqnqnqnr'