o ¾e¦i—+ã@s2ddlmZddlmZGdd„deƒZdgZdS)é)ÚPreTrainedConfig)ÚRopeParametersc2s8eZdZdZdZdgZdddddœZ                 d.dedBdedBdedBdedBdedBdedBdedBdedBdedBdedBdedBdedBd edBd!e dBd"edBd#e dBd$e e ee fBdBd%e dBd&e dBd'e dBd(edBd)edBd*edBd+e dBf0‡fd,d-„ Z ‡ZS)/ÚMoonshineConfiga7 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 ```Ú moonshineÚpast_key_valuesÚdecoder_num_key_value_headsÚdecoder_num_attention_headsÚdecoder_num_hidden_layersÚdecoder_hidden_act)Únum_key_value_headsÚnum_attention_headsÚnum_hidden_layersÚ hidden_acté€é é€ééNÚgeluÚsiluéç{®Gáz”?éTFçéÚ vocab_sizeÚ hidden_sizeÚintermediate_sizeÚencoder_num_hidden_layersÚencoder_num_attention_headsÚencoder_num_key_value_headsÚpad_head_dim_to_multiple_ofÚencoder_hidden_actÚmax_position_embeddingsÚinitializer_rangeÚdecoder_start_token_idÚ use_cacheÚrope_parametersÚis_encoder_decoderÚattention_biasÚattention_dropoutÚ bos_token_idÚ eos_token_idÚ pad_token_idÚtie_word_embeddingsc sÔ||_||_||_||_||_||_||_|dur|}||_| dur$|} | |_| |_ | |_ | |_ | |_ ||_ ||_||_||_||_||_||_||_||_||_||_||_| dd¡tƒjdd|i|¤ŽdS)NÚpartial_rotary_factorgÍÌÌÌÌÌì?r(©)rrrrr rrr rr!r"r r#r$r%r&r(r)r*r+r,r-r.r'Ú setdefaultÚsuperÚ__init__)Úselfrrrrr rrr rr!r"r r#r$r%r&r'r(r)r*r+r,r-r.Úkwargs©Ú __class__r0ús/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/transformers/models/moonshine/configuration_moonshine.pyr3s> zMoonshineConfig.__init__)rrrrrrrNNNrrrrrTNTFrrrNT)Ú__name__Ú __module__Ú __qualname__Ú__doc__Ú model_typeÚkeys_to_ignore_at_inferenceÚ attribute_mapÚintÚstrÚfloatÚboolrÚdictr3Ú __classcell__r0r0r6r8rs¤\ü çþýüûúùø ÷ ö õ ô óòñðïîíìëêéèçrN)Úconfiguration_utilsrÚmodeling_rope_utilsrrÚ__all__r0r0r0r8Ús   )