fTh\SSKJr SSKJr SSKJr SSKJr \R"\ 5r "SS\5r "SS \5r SS /r g ) )PretrainedConfig)rope_config_validation)logging) AutoConfigcj^\rSrSrSrSrSrS/rSU4SjjrSr U=r $) CsmDepthDecoderConfiga This is the configuration class to store the configuration of a [`CsmDepthDecoderModel`]. It is used to instantiate an CSM depth decoder 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 csm-1b. e.g. [eustlb/csm-1b](https://huggingface.co/eustlb/csm-1b) Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: num_codebooks (`int`, *optional*, defaults to 32): Number of codebooks used in the underlying codec model responsible for tokenizing the audio. backbone_hidden_size (`int`, *optional*, defaults to 2048): Dimension of the hidden representations of the backbone model used with this depth decoder. vocab_size (`int`, *optional*, defaults to 2051): Vocabulary size of the CsmDepthDecoder model. Defines the number of different audio tokens that can be represented by each codebook. hidden_size (`int`, *optional*, defaults to 1024): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 8192): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 4): Number of hidden layers in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to 8): Number of attention heads for each attention layer in the Transformer decoder. num_key_value_heads (`int`, *optional*, defaults to 2): 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 checkout [this paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `num_attention_heads`. 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 33): 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. rms_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the rms normalization layers. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. pad_token_id (`int`, *optional*, defaults to 2050): Padding token id. bos_token_id (`int`, *optional*): Beginning of stream token id. eos_token_id (`int`, *optional*): End of stream token id. rope_theta (`float`, *optional*, defaults to 500000): The base period of the RoPE embeddings. rope_scaling (`Dict`, *optional*): Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value accordingly. Expected contents: `rope_type` (`str`): The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope', 'llama3'], with 'default' being the original RoPE implementation. `factor` (`float`, *optional*): Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In most scaling types, a `factor` of x will enable the model to handle sequences of length x * original maximum pre-trained length. `original_max_position_embeddings` (`int`, *optional*): Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during pretraining. `attention_factor` (`float`, *optional*): Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention computation. If unspecified, it defaults to value recommended by the implementation, using the `factor` field to infer the suggested value. `beta_fast` (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear ramp function. If unspecified, it defaults to 32. `beta_slow` (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear ramp function. If unspecified, it defaults to 1. `short_factor` (`List[float]`, *optional*): Only used with 'longrope'. The scaling factor to be applied to short contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 `long_factor` (`List[float]`, *optional*): Only used with 'longrope'. The scaling factor to be applied to long contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 `low_freq_factor` (`float`, *optional*): Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE `high_freq_factor` (`float`, *optional*): Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE 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. mlp_bias (`bool`, *optional*, defaults to `False`): Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers. head_dim (`int`, *optional*): The attention head dimension. If None, it will default to hidden_size // num_attention_heads ```python >>> from transformers import CsmDepthDecoder, CsmDepthDecoderConfig >>> # Initializing a CsmDepthDecoder >>> configuration = CsmDepthDecoderConfig() >>> model = CsmDepthDecoderModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```csm_depth_decoder_modeldepth_decoder_configpast_key_valuesc .>URSS5(a [S5e[TU] "SUUUSS.UD6 XlX0lX lXlX@lXPl X`l Xpl UcUnXl Xl XlXlXlUUlUUlUUlUUlUUlUbUOURUR-UlUR$b,SUR$;aUR$SUR$S'[/U5 g)Ntie_word_embeddingsFzE`tie_word_embeddings=True` is not supported for CsmDepthDecoderConfig pad_token_id bos_token_id eos_token_idrtype rope_type)pop ValueErrorsuper__init__ num_codebooks vocab_sizebackbone_hidden_sizemax_position_embeddings hidden_sizeintermediate_sizenum_hidden_layersnum_attention_headsnum_key_value_heads hidden_actinitializer_range rms_norm_eps use_cache rope_theta rope_scalingattention_biasattention_dropoutmlp_biashead_dimr)selfrrrrr r!r"r#r$rr%r&r'rrrr(r)r*r+r,r-kwargs __class__s a/var/www/auris/envauris/lib/python3.13/site-packages/transformers/models/csm/configuration_csm.pyrCsmDepthDecoderConfig.__init__s*4 ::+U 3 3de e  %%% %    +$$8!'>$&!2!2#6   &"5 #6 $!2("$(,!2  $,$8d>N>NRVRjRj>j     (Vt7H7H-H-1->->v-FD  k *t$)r*r+rr-r$rr%r rr,r"rr!r#r&r)r(r'r) i rsilu!{Gz?h㈵>TNNN NFFN) __name__ __module__ __qualname____firstlineno____doc__ model_typebase_config_keykeys_to_ignore_at_inferencer__static_attributes__ __classcell__r0s@r1r r sol \+J,O#4"5! "/@%@%r3r c^\rSrSrSrSrSrS/r\\ S.r S U4Sjjr Sr U=r $) CsmConfiga This is the configuration class to store the configuration of a [`CsmForConditionalGeneration`]. It is used to instantiate an CSM 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 csm-1b. e.g. [eustlb/csm-1b](https://huggingface.co/eustlb/csm-1b) Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: num_codebooks (`int`, *optional*, defaults to 32): Number of codebooks used in the underlying codec model responsible for tokenizing the audio. vocab_size (`int`, *optional*, defaults to 2051): Vocabulary size of the Csm model. Defines the number of different audio tokens that can be represented by each codebook. text_vocab_size (`int`, *optional*, defaults to 128256): Vocabulary size of the text input for the Csm model. Defines the number of different text tokens that can be represented. hidden_size (`int`, *optional*, defaults to 2048): Dimension of the hidden representations of the backbone model. intermediate_size (`int`, *optional*, defaults to 8192): Dimension of the MLP representations of the backbone model. num_hidden_layers (`int`, *optional*, defaults to 16): Number of hidden layers in the backbone model Transformer decoder. num_attention_heads (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer in the backbone model Transformer decoder. num_key_value_heads (`int`, *optional*, defaults to 8): 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 checkout [this paper](https://arxiv.org/pdf/2305.13245.pdf). hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the backbone model Transformer decoder. max_position_embeddings (`int`, *optional*, defaults to 2048): 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. rms_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the rms normalization layers. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. pad_token_id (`int`, *optional*, defaults to 128002): Padding token id. codebook_pad_token_id (`int`, *optional*, defaults to 2050): Padding token id for codebook tokens. codebook_eos_token_id (`int`, *optional*, defaults to 0): End of stream token id for codebook tokens. bos_token_id (`int`, *optional*, defaults to 128000): Beginning of stream token id. eos_token_id (`int`, *optional*): End of stream token id. audio_token_id (`int`, *optional*, defaults to 128002): Audio token id in the text input. audio_eos_token_id (`int`, *optional*, defaults to 128003): End of stream token id for audio in the text input. rope_theta (`float`, *optional*, defaults to 500000): The base period of the RoPE embeddings. rope_scaling (`Dict`, *optional*, defaults to `{'factor': 32.0, 'high_freq_factor': 0.5, 'low_freq_factor': 0.125, 'original_max_position_embeddings': 1024, 'rope_type': 'llama3'}`): Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value accordingly. Expected contents: `rope_type` (`str`): The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope', 'llama3'], with 'default' being the original RoPE implementation. `factor` (`float`, *optional*): Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In most scaling types, a `factor` of x will enable the model to handle sequences of length x * original maximum pre-trained length. `original_max_position_embeddings` (`int`, *optional*): Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during pretraining. `attention_factor` (`float`, *optional*): Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention computation. If unspecified, it defaults to value recommended by the implementation, using the `factor` field to infer the suggested value. `beta_fast` (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear ramp function. If unspecified, it defaults to 32. `beta_slow` (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear ramp function. If unspecified, it defaults to 1. `short_factor` (`List[float]`, *optional*): Only used with 'longrope'. The scaling factor to be applied to short contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 `long_factor` (`List[float]`, *optional*): Only used with 'longrope'. The scaling factor to be applied to long contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 `low_freq_factor` (`float`, *optional*): Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE `high_freq_factor` (`float`, *optional*): Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE 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. mlp_bias (`bool`, *optional*, defaults to `False`): Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers. head_dim (`int`, *optional*): The attention head dimension. If None, it will default to hidden_size // num_attention_heads tie_codebooks_embeddings (`bool`, *optional*, defaults to `True`): Whether to tie the codebook tokens embeddings of the backbone model to the codebook tokens embeddings of the depth decoder. depth_decoder_config (`CsmDepthDecoderConfig`, *optional*): Configuration for the depth decoder. codec_config (`PretrainedConfig`, *optional*): Configuration for the codec. ```python >>> from transformers import CsmForConditionalGeneration, CsmConfig >>> # Initializing a CsmConfig >>> configuration = CsmConfig() >>> # Initializing a model >>> model = CsmForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```csm csm_configr ) codec_configr c H>URSS5(a [S5e[TU] "S UUUSS.UD6 Uc%[ 5Ul[ RS5 OB[U[5(a[ S 0UD6UlO[U[5(aUUlUc1[R"S5Ul [ RS5 OM[U[5(a[R"S 0UD6Ul O[U[5(aUUl X0lXlUUlUUlXlUUlUUlX lXlX@lXPlX`lXplUcUnXlXlXlXlXlUUl UUl!UUl"UUl#UUl$UbUOUR.UR4-Ul%URBb,SURB;aURBSURBS '[MU5 g) NrFz9`tie_word_embeddings=True` is not supported for CsmConfigrzAdepth_decoder_config is None, using default depth decoder config.mimiz9codec_config is None, using default audio encoder config.rrr)'rrrrr r loggerinfo isinstancedictr for_modelrPrtext_vocab_sizeraudio_token_idaudio_eos_token_idcodebook_pad_token_idcodebook_eos_token_idtie_codebooks_embeddingsrrrr r!r"r#r$r%r&r'r(r)r*r+r,r-r) r.rrrXrr r!r"r#r$rr%r&r'rr[r\rrrYrZr(r)r*r+r,r-r]r rPr/r0s r1rCsmConfig.__init__TsB ::+U 3 3XY Y  %%% %     '(=(?D % KK[ \ ,d 3 3(=(U@T(UD % ,.C D D($&!2!2#6   &"5 #6 $!2("$(,!2  $,$8d>N>NRVRjRj>j     (Vt7H7H-H-1->->v-FD  k *t$r3)r*r+rZrYr\r[rPr r-r$rr%r rr,r"rr!r#r&r)r(rXr]r'r)r4r6ir5r7r4r9r:r5r<r=TiiNr`ir>NFr?FNTNN)r@rArBrCrDrErFrGrr sub_configsrrHrIrJs@r1rLrLsz xJ"O#4"5" 5K $"!!%!=^%^%r3rLN)configuration_utilsrmodeling_rope_utilsrutilsrauto.configuration_autor get_loggerr@rSr rL__all__rr3r1risT 490   H %s%,s%lc% c%N r3