qi"TddlmZddlmZmZGddeZGddeZddgZy) )PreTrainedConfig)CONFIG_MAPPING AutoConfigc>eZdZdZdZ dfd ZxZS)GlmAsrEncoderConfiga` This is the configuration class to store the configuration of a [`GlmAsrEncoder`]. It is used to instantiate a glmasr audio encoder according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the audio encoder of the glmasr architecture. e.g. [zai-org/GLM-ASR-Nano-2512](https://huggingface.co/zai-org/GLM-ASR-Nano-2512) Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PreTrainedConfig`] for more information. Args: hidden_size (`int`, *optional*, defaults to 1280): Dimensionality of the hidden representations. intermediate_size (`int`, *optional*, defaults to 5120): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 20): Number of attention heads for each attention layer in the Transformer encoder. num_key_value_heads (`int`, *optional*): 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, check out [this paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `num_attention_heads`. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. max_position_embeddings (`int`, *optional*, defaults to 1500): The maximum sequence length that this model might ever be used with. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. rope_parameters (`RopeParameters`, *optional*): Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE with longer `max_position_embeddings`. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. num_mel_bins (`int`, *optional*, defaults to 128): Number of mel features used per input features. Should correspond to the value used in the `GlmAsrProcessor` class. ```python >>> from transformers import GlmAsrEncoderConfig, GlmAsrEncoder >>> # Initializing a GlmAsrEncoderConfig >>> configuration = GlmAsrEncoderConfig() >>> # Initializing a GlmAsrEncoder (with random weights) >>> model = GlmAsrEncoder(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```glmasr_encoderc  ||_||_||_||_||}||_||_||_||z|_||_| |_ | |_ | |_ | jddt |8di| y)Npartial_rotary_factorg?) hidden_sizeintermediate_sizenum_hidden_layersnum_attention_headsnum_key_value_heads hidden_actinitializer_rangehead_dimmax_position_embeddingsrope_parametersattention_dropout num_mel_bins setdefaultsuper__init__)selfr rrrrrrrrrrkwargs __class__s a/opt/pipecat/venv/lib/python3.12/site-packages/transformers/models/glmasr/configuration_glmasr.pyrzGlmAsrEncoderConfig.__init__Os'!2!2#6  &"5 #6 $!2#':: '>$.!2(137 "6") ii Ngeluig{Gz?Ng)__name__ __module__ __qualname____doc__ model_typer __classcell__rs@rrrs=7 r"J  $##r rc `eZdZdZdZeedZdddddd d d d gd dddd Z dfd ZxZ S) GlmAsrConfiga This is the configuration class to store the configuration of a [`GlmAsrForConditionalGeneration`]. It is used to instantiate an glmasr 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 glmasr-Mini-3B. e.g. [zai-org/GLM-ASR-Nano-2512](https://huggingface.co/zai-org/GLM-ASR-Nano-2512) Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PreTrainedConfig`] for more information. Args: audio_config (`Union[AutoConfig, dict]`, *optional*): The config object or dictionary of the audio encoder. text_config (`Union[AutoConfig, dict]`, *optional*): The config object or dictionary of the text model. audio_token_id (`int`, *optional*, defaults to 59260): The audio token index to encode the audio prompt. projector_hidden_act (`str`, *optional*, defaults to `"gelu"`): The activation function (function or string) in the multi-modal projector. ```python >>> from transformers import GlmAsrForConditionalGeneration, GlmAsrConfig >>> # Initializing a glmasr configuration >>> configuration = GlmAsrConfig() >>> # Initializing a GLM-ASR-Nano-2512 model with random weights >>> model = GlmAsrForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```glmasr) text_config audio_configiiii gh㈵>T)iniuiwg@default) rope_theta rope_type) vocab_sizer rrrrr rms_norm_eps use_cache eos_token_idrc t|tr'|jdd|d<t|ddi|}n| td}||_t|tr5|jdd|d<t|ddii|j |}n|tddi|j }||_|j|_|j|_||_ ||_ t|0di|y)Nr)r llamar ) isinstancedictgetrr0_default_text_config_kwargsr/r7r audio_token_idprojector_hidden_actrr)rr0r/rArBrrs rrzGlmAsrConfig.__init__s lD ))5)9)9,HX)YL &),|*DEU UL  !)*:;=L( k4 ((3 g(NK %(\)BCET55EEK (1UD4T4TUK&%00&22,$8! "6"r )NNi|r#) r%r&r'r(r)r sub_configsr@rr*r+s@rr-r-qsb BJ",jIK!! #'-*1 J # # ##r r-N)configuration_utilsrautorrrr-__all__r r rrGs94-[#*[#|P##P#f !. 1r