o ¾e¦i­Fã@sfddlmZddlmZddlmZddlmZe e ¡Z Gdd„deƒZ Gdd „d eƒZ dd gZ d S) é)ÚPreTrainedConfig)ÚRopeParameters)Úloggingé)Ú AutoConfigc,seZdZdZdZdZdgZdZ                d-dedBdedBdedBdedBdedBdedBdedBdedBdedBdedBd e dBd!edBd"e dBd#edBd$edBd%edBd&e e e e fBdBd'e dBd(e dBd)e dBd*edBf*‡fd+d,„ Z‡ZS).ÚCsmDepthDecoderConfiga| 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. [sesame/csm-1b](https://huggingface.co/sesame/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, 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 `"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_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_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 ```Úcsm_depth_decoder_modelÚdepth_decoder_configÚpast_key_values瀄Aé éééé éérÚsilué!ç{®Gáz”?çñh㈵øä>TNFçÚ num_codebooksÚbackbone_hidden_sizeÚ vocab_sizeÚ hidden_sizeÚintermediate_sizeÚnum_hidden_layersÚnum_attention_headsÚnum_key_value_headsÚ hidden_actÚmax_position_embeddingsÚinitializer_rangeÚ rms_norm_epsÚ use_cacheÚ pad_token_idÚ bos_token_idÚ eos_token_idÚrope_parametersÚattention_biasÚattention_dropoutÚmlp_biasÚhead_dimc sÈ| dd¡r tdƒ‚||_||_||_||_||_||_| |_||_ ||_ ||_ ||_ |dur1|}||_ | |_| |_| |_| |_||_||_||_|durO|n|j |j |_||_tƒjdi|¤ŽdS)NÚtie_word_embeddingsFzE`tie_word_embeddings=True` is not supported for CsmDepthDecoderConfig©)ÚpopÚ ValueErrorr%r&r'rrrr!rrrrrr r"r#r$r)r*r+r,r(ÚsuperÚ__init__)Úselfrrrrrrrrr r!r"r#r$r%r&r'r(r)r*r+r,Úkwargs©Ú __class__r.úg/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/transformers/models/csm/configuration_csm.pyr2js4 zCsmDepthDecoderConfig.__init__)r r rrrrrrrrrrTNNNNFrFN)Ú__name__Ú __module__Ú __qualname__Ú__doc__Ú model_typeÚbase_config_keyÚkeys_to_ignore_at_inferenceÚ default_thetaÚintÚfloatÚboolrÚdictÚstrr2Ú __classcell__r.r.r5r7rsŒKêþýüûúùø ÷ ö õ ô óòñðïîíìëêrc:sdeZdZdZdZdZdgZdZee dœZ                      d8de dBde dBde dBde dBde dBde dBd e dBd!e dBd"e dBd#e dBd$e dBd%e dBd&edBd'e dBd(e dBd)e dBd*e dBd+e dBd,e dBd-e dBd.eee efBdBd/edBd0e dBd1edBd2e dBd3edBd4edBd5edBf8‡fd6d7„ Z‡ZS)9Ú CsmConfigaW 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. [sesame/csm-1b](https://huggingface.co/sesame/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, check out [this paper](https://huggingface.co/papers/2305.13245). 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_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_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 ```ÚcsmÚ csm_configr r )Ú codec_configr r réõr rérrrrTéôéééôNéôFrrrÚtext_vocab_sizerrrrrr r!r"r#r$r%Úcodebook_pad_token_idÚcodebook_eos_token_idr&r'Úaudio_token_idÚaudio_eos_token_idr(r)r*r+r,Útie_codebooks_embeddingsr rIc s‚| dd¡r tdƒ‚|durtƒ|_t d¡nt|tƒr&tdi|¤Ž|_nt|tƒr.||_|dur>t  d¡|_ t d¡nt|tƒrMtj di|¤Ž|_ nt|t ƒrU||_ ||_ ||_ ||_||_||_||_||_||_| |_||_||_||_||_|dur‚|}||_| |_| |_| |_| |_||_||_||_ |dur |n|j|j|_!||_"||_#||_$||_%d|_&t'ƒj(di|¤ŽdS)Nr-Fz9`tie_word_embeddings=True` is not supported for CsmConfigzAdepth_decoder_config is None, using default depth decoder config.Úmimiz9codec_config is None, using default audio encoder config.r.))r/r0rr ÚloggerÚinfoÚ isinstancerCrÚ for_modelrIrrQrrTrUrRrSrVrr!rrrrrr r"r#r$r)r*r+r,r(r%r&r'r-r1r2)r3rrrQrrrrrr r!r"r#r$r%rRrSr&r'rTrUr(r)r*r+r,rVr rIr4r5r.r7r2s\        zCsmConfig.__init__)r rrJr rrKr rrr rrTrLrMrNrONrLrPNFrFNTNN)r8r9r:r;r<r=r>r?rrÚ sub_configsr@rDrArBrrCr2rEr.r.r5r7rF£s¼Yþãþýüûúùø ÷ ö õ ô óòñðïîíìëêéèçæåäãrFN)Úconfiguration_utilsrÚmodeling_rope_utilsrÚutilsrÚauto.configuration_autorÚ get_loggerr8rXrrFÚ__all__r.r.r.r7Ús      =þ