qihdZddlZddlmZddlmZej eZGddeZ dgZ y)zMAMBA configurationN)PreTrainedConfig)loggingc\eZdZdZdZ dfd ZxZS) MambaConfiga: This is the configuration class to store the configuration of a [`MambaModel`]. It is used to instantiate a MAMBA 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 MAMBA [state-spaces/mamba-2.8b](https://huggingface.co/state-spaces/mamba-2.8b) architecture. 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 50280): Vocabulary size of the MAMBA model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`MambaModel`]. hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the embeddings and hidden states. state_size (`int`, *optional*, defaults to 16): shape of the state space latents. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the model. layer_norm_epsilon (`float`, *optional*, defaults to 1e-05): The epsilon to use in the layer normalization layers. pad_token_id (`int`, *optional*, defaults to 0): Padding token id. bos_token_id (`int`, *optional*, defaults to 0): The id of the beginning of sentence token in the vocabulary. eos_token_id (`int`, *optional*, defaults to 0): The id of the end of sentence token in the vocabulary. expand (`int`, *optional*, defaults to 2): Expanding factor used to determine the intermediate size. conv_kernel (`int`, *optional*, defaults to 4): Size of the convolution kernel. use_bias (`bool`, *optional*, defaults to `False`): Whether or not to use bias in ["in_proj", "out_proj"] of the mixer block use_conv_bias (`bool`, *optional*, defaults to `True`): Whether or not to use bias in the convolution layer of the mixer block. hidden_act (`str`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the decoder. initializer_range (`float`, *optional*, defaults to 0.1): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. residual_in_fp32 (`bool`, *optional*, defaults to `True`): Whether or not residuals should be in `float32`. If set to `False` residuals will keep the same `dtype` as the rest of the model time_step_rank (`Union[int,str]`, *optional*, defaults to `"auto"`): Rank of the discretization projection matrix. `"auto"` means that it will default to `math.ceil(self.hidden_size / 16)` time_step_scale (`float`, *optional*, defaults to 1.0): Scale used used to scale `dt_proj.bias`. time_step_min (`float`, *optional*, defaults to 0.001): Minimum `time_step` used to bound `dt_proj.bias`. time_step_max (`float`, *optional*, defaults to 0.1): Maximum `time_step` used to bound `dt_proj.bias`. time_step_init_scheme (`float`, *optional*, defaults to `"random"`): Init scheme used for `dt_proj.weight`. Should be one of `["random","uniform"]` time_step_floor (`float`, *optional*, defaults to 0.0001): Minimum clamping value of the `dt_proj.bias` layer initialization. rescale_prenorm_residual (`bool`, *optional*, defaults to `False`): Whether or not to rescale `out_proj` weights when initializing. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the cache should be used. use_mambapy (`bool`, *optional*, defaults to `False`): Determines the fallback strategy during training if the CUDA-based official implementation of Mamba is not available. If `True`, the mamba.py implementation is used. If `False`, the naive and slower implementation is used. Consider switching to the naive version if memory is limited. use_associative_scan (`bool`, *optional*, defaults to `True`): Whether to use PyTorch's `torch._higher_order_ops.associative_scan` for the parallel scan instead of the naive sequential implementation. The associative scan is only active during `torch.compile` tracing and requires torch >= 2.9.0. Both paths are tested to produce numerically identical results (see `test_associative_scan_matches_sequential`). Set to `False` to fall back to the sequential loop. tie_word_embeddings (`bool`, *optional*, defaults to `True`): Whether to tie weight embeddings Example: ```python >>> from transformers import MambaConfig, MambaModel >>> # Initializing a Mamba configuration >>> configuration = MambaConfig() >>> # Initializing a model (with random weights) from the configuration >>> model = MambaModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```mambac ||_||_||_||_||_| |_| |_t| |jz|_||_ ||_ ||_ | |_ | |_ | |_||_|dk(r"t!j"|jdz n||_||_||_||_||_||_||_||_||_||_||_||_t=||di|y)Nauto) vocab_size hidden_size state_sizenum_hidden_layerslayer_norm_epsilon conv_kernelexpandintintermediate_size bos_token_id eos_token_id pad_token_iduse_bias use_conv_bias hidden_actinitializer_rangemathceiltime_step_ranktime_step_scale time_step_min time_step_maxtime_step_init_schemetime_step_floorrescale_prenorm_residualresidual_in_fp32 use_cache use_mambapyuse_associative_scantie_word_embeddingssuper__init__)selfr rrrrrrrrrrrrrr&rr r!r"r#r$r%r'r(r)r*kwargs __class__s _/opt/pipecat/venv/lib/python3.12/site-packages/transformers/models/mamba/configuration_mamba.pyr,zMambaConfig.__init__ls<%&$!2"4& !$Vd.>.>%>!?(((  *$!2BPTZBZdii(8(82(=>`n.**%:".(@% 0"&$8!#6  "6")ihir gh㈵>rrrFTsilu皙?Tr g?gMbP?r6randomg-C6?FTFTT)__name__ __module__ __qualname____doc__ model_typer, __classcell__)r/s@r0rrskN `J&!&! 7:#:#r1r) r;rconfiguration_utilsrutilsr get_loggerr8loggerr__all__r r1r0rCs@ 3   H %M#"M#` /r1