o ¾e¦itã@sHdZddlZddlmZddlmZe e¡ZGdd„deƒZ dgZ dS)zMAMBA configurationéNé)ÚPreTrainedConfig)ÚloggingcsXeZdZdZdZ                d‡fdd„ Z‡ZS)Ú MambaConfiga4 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. 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 ```ÚmambaéhÄééé çñh㈵øä>rééFTÚsiluçš™™™™™¹?Úautoçð?çü©ñÒMbP?Úrandomç-Cëâ6?c sÔ||_||_||_||_||_| |_| |_t| |jƒ|_||_ ||_ ||_ | |_ | |_ | |_||_|dkr>t |jd¡n||_||_||_||_||_||_||_||_||_||_||_tƒjdi|¤ŽdS)Nrr ©)Ú vocab_sizeÚ hidden_sizeÚ state_sizeÚnum_hidden_layersÚlayer_norm_epsilonÚ conv_kernelÚexpandÚintÚintermediate_sizeÚ bos_token_idÚ eos_token_idÚ pad_token_idÚuse_biasÚ use_conv_biasÚ hidden_actÚinitializer_rangeÚmathÚceilÚtime_step_rankÚtime_step_scaleÚ time_step_minÚ time_step_maxÚtime_step_init_schemeÚtime_step_floorÚrescale_prenorm_residualÚresidual_in_fp32Ú use_cacheÚ use_mambapyÚtie_word_embeddingsÚsuperÚ__init__)Úselfrrrrrr!rr rrr"r#r$r%r/r(r)r*r+r,r-r.r0r1r2Úkwargs©Ú __class__rúk/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/transformers/models/mamba/configuration_mamba.pyr4gs6zMambaConfig.__init__)rrr r r rrrr r FTrrTrrrrrrFTFT)Ú__name__Ú __module__Ú __qualname__Ú__doc__Ú model_typer4Ú __classcell__rrr7r9rs:Kær) r=r&Úconfiguration_utilsrÚutilsrÚ get_loggerr:ÚloggerrÚ__all__rrrr9Ús