qi+8ddlmZddlmZGddeZdgZy))PreTrainedConfig)RopeParametersc2neZdZdZdZdgZdddddZ dd ed zd ed zd ed zd ed zded zded zded zded zded zded zded zded zded zde d zded zde d zde e ee fzd zde d zde d zde d zded zded zded zde d zf0fd Z xZS) MoonshineConfiga7 This is the configuration class to store the configuration of a [`MoonshineModel`]. It is used to instantiate a Moonshine 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 Moonshine [UsefulSensors/moonshine-tiny](https://huggingface.co/UsefulSensors/moonshine-tiny). 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 32768): Vocabulary size of the Moonshine model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`MoonshineModel`]. hidden_size (`int`, *optional*, defaults to 288): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 1152): Dimension of the MLP representations. encoder_num_hidden_layers (`int`, *optional*, defaults to 6): Number of hidden layers in the Transformer encoder. decoder_num_hidden_layers (`int`, *optional*, defaults to 6): Number of hidden layers in the Transformer decoder. encoder_num_attention_heads (`int`, *optional*, defaults to 8): Number of attention heads for each attention layer in the Transformer encoder. decoder_num_attention_heads (`int`, *optional*, defaults to 8): Number of attention heads for each attention layer in the Transformer decoder. 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 `encoder_num_key_value_heads=encoder_num_attention_heads`, the model will use Multi Head Attention (MHA), if `encoder_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`. decoder_num_key_value_heads (`int`, *optional*): This is the number of key_value heads that should be used to implement Grouped Query Attention. If `decoder_num_key_value_heads=decoder_num_attention_heads`, the model will use Multi Head Attention (MHA), if `decoder_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 `decoder_num_attention_heads`. pad_head_dim_to_multiple_of (`int`, *optional*): Pad head dimension in encoder and decoder to the next multiple of this value. Necessary for using certain optimized attention implementations. encoder_hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder. decoder_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 512): 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. decoder_start_token_id (`int`, *optional*, defaults to 1): Corresponds to the "<|startoftranscript|>" token, which is automatically used when no `decoder_input_ids` are provided to the `generate` function. It is used to guide the model`s generation process depending on the task. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). 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`. is_encoder_decoder (`bool`, *optional*, defaults to `True`): Whether the model is used as an encoder/decoder or not. attention_bias (`bool`, *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. bos_token_id (`int`, *optional*, defaults to 1): Denotes beginning of sequences token id. eos_token_id (`int`, *optional*, defaults to 2): Denotes end of sequences token id. pad_token_id (`int`, *optional*): Padding token id. tie_word_embeddings (`bool`, *optional*, defaults to `True`): Whether to tie weight embeddings Example: ```python >>> from transformers import MoonshineModel, MoonshineConfig >>> # Initializing a Moonshine style configuration >>> configuration = MoonshineConfig().from_pretrained("UsefulSensors/moonshine-tiny") >>> # Initializing a model from the configuration >>> model = MoonshineModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` moonshinepast_key_valuesdecoder_num_key_value_headsdecoder_num_attention_headsdecoder_num_hidden_layersdecoder_hidden_act)num_key_value_headsnum_attention_headsnum_hidden_layers hidden_actN vocab_size hidden_sizeintermediate_sizeencoder_num_hidden_layersencoder_num_attention_headsencoder_num_key_value_headspad_head_dim_to_multiple_ofencoder_hidden_actmax_position_embeddingsinitializer_rangedecoder_start_token_id use_cacherope_parametersis_encoder_decoderattention_biasattention_dropout bos_token_id eos_token_id pad_token_idtie_word_embeddingsc ||_||_||_||_||_||_||_||}||_| |} | |_| |_ | |_ | |_ | |_ ||_ ||_||_||_||_||_||_||_||_||_||_||_|j1ddt3|hdd|i|y)Npartial_rotary_factorg?r)rrrrr rr rr rrr rrrrrrr r!r"r#r$r setdefaultsuper__init__)selfrrrrr rr rr rrr rrrrrrrr r!r"r#r$kwargs __class__s g/opt/pipecat/venv/lib/python3.12/site-packages/transformers/models/moonshine/configuration_moonshine.pyr*zMoonshineConfig.__init__s 8%&!2)B&)B&+F(+F( & .*E '+F( & .*E '+F(+F("4"4'>$!2&<#""4,!2(((&<##6 .137 I,>I&I)ii ir0r1NNNgelusiluig{Gz?TNTFgr4NT)__name__ __module__ __qualname____doc__ model_typekeys_to_ignore_at_inference attribute_mapintstrfloatboolrdictr* __classcell__)r-s@r.rrsZ xJ#4"5<<8* M"'"%(,01012323262626)/)/.1*.-.!%MQ*.&+*-#$#$#'+/3?J$J?J4Z?J: ?J $': ?J $': ?J&)4Z?J&)4Z?J&)4Z?J&)4Z?J&)4Z?J $J?J $J?J"%t?J!4<?J !$d !?J"$;#?J$($sN/B*CCdJ%?J&!4K'?J(t )?J*!4<+?J,Dj-?J.Dj/?J0Dj1?J2"D[3?J?Jr/rN)configuration_utilsrmodeling_rope_utilsrr__all__r'r/r.rFs)*41eJ&eJP  r/