ZjSrSSKJr SSKJr SSKJr SSKJrJ r \ R"\ 5r \"SS9\"S S \555r \"SS9\"S S \555rS S /rg )zchameleon model configuration)strict)PreTrainedConfig)RopeParameters)auto_docstringloggingzfacebook/chameleon-7b) checkpointc\rSrSr%SrSrSrSr\\ S'Sr \\ S'S r \ \ S 'Sr \\ S 'S r\\ S 'Sr\\ S'Sr\\ S'Sr\\\\S4-\ S'Sr\\ S'Sr\\S-\ S'Sr\\-\ S'Sr\\ S'SrSrg)ChameleonVQVAEConfiga| resolution (`int`, *optional*, defaults to 512): Resolution of the input images. 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 chameleon_vqgan vq_config embed_dimi num_embeddingsF double_latentlatent_channelsi resolutionr in_channels base_channels)rr.channel_multiplierrnum_res_blocksNattn_resolutionsdropoutvanilla attn_type{Gz?)__name__ __module__ __qualname____firstlineno____doc__ model_typebase_config_keyrint__annotations__rrboolrrrrrlisttuplerrrfloatr!strinitializer_range__static_attributes__r#چ/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/chameleon/configuration_chameleon.pyr r s"#J!OIsNCM4OSJKM36ES E#s(O3ENC)-d3i$&-GUS[Isr4r c^\rSrSr%SrSrS\0rS/rSr \ \ S'Sr \ \ S 'S r \ \ S 'S r\ \ S 'S r\ \ S'S r\ S-\ S'Sr\\ S'Sr\ \ S'Sr\\ S'Sr\\ S'Sr\\ S'Sr\ S-\ S'Sr\ S-\ S'Sr\ \\ -S-\ S'Sr\\ S 'Sr\\ -S-\ S!'Sr!\S-\ S"'S#r"\\ -S-\ S$'Sr#\ S-\ S%'Sr$\S-\ S&'Sr%\ \&-S-\ S'Sr'\ S-\ S''Sr(\\ S('U4S)jr)S*r*U=r+$)+ChameleonConfig@aa 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. vocabulary_map (`dict`, *optional*): A dictionary containing the vocabulary map from the tokenizer. Used to obtain tokens from the image inputs. ```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 ``` chameleonrpast_key_valuesi vocab_sizei hidden_sizei+intermediate_size num_hidden_layersnum_attention_headsNnum_key_value_headssilu hidden_actmax_position_embeddingsr"r2gh㈵> rms_norm_epsT use_cache pad_token_idr bos_token_idr eos_token_idFtie_word_embeddingsrope_parametersattention_biasrattention_dropoutmodel_parallel_size swin_normvocabulary_mapmlp_biasc Z>URc%[RS5 [5UlO9[ UR[ 5(a[S0URD6UlUR bUR RS5OSUl[TU]("S0UD6 g)NzJvq_config is None. initializing the ChameleonVQConfig with default values.zr#) rloggerinfor isinstancedictrPgetimage_token_idsuper __post_init__)selfkwargs __class__s r5rZChameleonConfig.__post_init__vs >> ! KKd e13DN  - -1CDNNCDNDHDWDWDcd1155i@im ''r4)rXr),r$r%r&r'r(r)r sub_configskeys_to_ignore_at_inferencer;r+r,r<r=r?r@rArCr1rDr2r0rErFr-rGrHrIr.rJrKrrVrLrMrNrOrrrPrQrZr3 __classcell__)r]s@r5r7r7@sc.J 45K#4"5JK"s"s!!&(t(J#'S'#u#L%It#L#*# L#* +,L#S /D(, %%48O^d*T18"'ND4K',/us{T)/&'t'"Itd{"04It&&-4"&ND4K&Hd ( (r4r7N)r(huggingface_hub.dataclassesrconfiguration_utilsrmodeling_rope_utilsrutilsrr get_loggerr$rSr r7__all__r#r4r5rhs$.31,   H %23!+!4!H23=(&=(4=(@ 4 5r4