o ¾e¦i©4ã@sTddlmZddlmZGdd„deƒZGdd„deƒZGdd„deƒZgd ¢Zd S) é)ÚPreTrainedConfig)ÚRopeParameterscsŽeZdZdZdZdZddddddddgd ¢d dgd d d fdededededededededeededeededede f‡fdd„ Z ‡Z S)ÚEmu3VQVAEConfiga This is the configuration class to store the configuration of a [`Emu3VQVAE`]. It is used to instantiate an VQ-VAE model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a configuration to the VQ model presented in Emu3 paper. Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PreTrainedConfig`] for more information. Args: codebook_size (`int`, *optional*, defaults to 32768): Codebook size of the VQ model. embed_dim (`int`, *optional*, defaults to 4): Dimension of the quantized vector in codebook. latent_channels (`int`, *optional*, defaults to 4): Dimension of the output channel of encoder and the input channel of decoder double_latent (`bool`, *optional*, defaults to `False`): Whether double the output dim of the encoder. in_channels (`int`, *optional*, defaults to 3): Input channel of encoder. out_channels (`int`, *optional*, defaults to 3): Output channel of decoder. temporal_downsample_factor (`int`, *optional*, defaults to 4): Temporal downsample factor. base_channels (`int`, *optional*, defaults to 256): Basic channel number of the intermediate blocks. channel_multiplier (`list[int]`, *optional*, defaults to `[1, 2, 2, 4]`): Channel scaling factor of the intermediate blocks. num_res_blocks (`int`, *optional*, defaults to 2): Residual block number in each stage. attn_resolutions (`list[int]`, *optional*, defaults to `[3]`): Stage indices to apply attention. hidden_size (`int`, *optional*, defaults to 1024): Dimension of the hidden representations in the attention layer. num_attention_heads (`int`, *optional*, defaults to 1): Number of attention heads for each attention layer. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. ```python >>> from transformers import Emu3VQVAE, Emu3VQVAEConfig >>> # Initializing a video VQ model of Emu3 configuration >>> configuration = Emu3VQVAEConfig() >>> # Initializing a model from the Emu3 VQ model style configuration >>> model = Emu3VQVAE(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```Ú emu3_vqganÚ vq_configi€éFré)éér rr ir gÚ codebook_sizeÚ embed_dimÚlatent_channelsÚ double_latentÚ in_channelsÚ out_channelsÚtemporal_downsample_factorÚ base_channelsÚchannel_multiplierÚnum_res_blocksÚattn_resolutionsÚ hidden_sizeÚnum_attention_headsÚattention_dropoutc sjtƒjdi|¤Ž||_||_||_||_||_||_||_||_ | |_ | |_ | |_ | |_ | |_||_dS©N©)ÚsuperÚ__init__r r r rrrrrrrrrrr)Úselfr r r rrrrrrrrrrrÚkwargs©Ú __class__rúi/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/transformers/models/emu3/configuration_emu3.pyrKs zEmu3VQVAEConfig.__init__) Ú__name__Ú __module__Ú __qualname__Ú__doc__Ú model_typeÚbase_config_keyÚintÚboolÚlistÚfloatrÚ __classcell__rrrr!rs^2ñþýüûúùø ÷ ö õ ô óòñrc$s¬eZdZdZdZdZdgZdZ             d)dededededededBde dede de d ed!ed"ed#e dBd$e d%e d&e dBf"‡fd'd(„ Z ‡ZS)*ÚEmu3TextConfigaÍ This is the configuration class to store the configuration of a [`Emu3TextModel`]. It is used to instantiate a emu3 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 [Emu3-community/Emu3-Chat-hf](https://huggingface.co/Emu3-community/Emu3-Chat-hf). 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 184622): Vocabulary size of the Emu3 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Emu3Model`] hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 14336): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer in the 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). 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 9216): The maximum sequence length that this model might ever be used with. Emu supports up to 9216 tokens, 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 151643): Padding token id. bos_token_id (`int`, *optional*, defaults to 151849): Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 151850): 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`. 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. 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 0.1): The dropout ratio for the attention probabilities. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie weight embeddings ```python >>> from transformers import Emu3Model, Emu3Config >>> # Initializing a Emu3-community/Emu3-Chat-hf style configuration >>> configuration = Emu3Config() >>> # Initializing a model from the Emu3-community/Emu3-Chat-hf style configuration >>> model = Emu3Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```Úemu3_text_modelÚ text_configÚpast_key_valuesg€„.Aé.Ñéé8é éÚsilué$çñh㈵øä>Té[Pé)Qé*QNFçš™™™™™¹?ç{®Gáz”?Ú vocab_sizerÚintermediate_sizeÚnum_hidden_layersrÚnum_key_value_headsÚ hidden_actÚmax_position_embeddingsÚ rms_norm_epsÚ use_cacheÚ pad_token_idÚ bos_token_idÚ eos_token_idÚrope_parametersrÚinitializer_rangeÚtie_word_embeddingsc sˆ||_||_||_||_||_||_||_||_| |_| |_ ||_ ||_ ||_ ||_ ||_| |_| |_| |_||_tƒjdi|¤ŽdSr)r>rCrr?r@rrArBrDrEÚmlp_biasÚattention_biasrJrrIrFrGrHrKrr)rr>rr?r@rrArBrCrDrErFrGrHrIrLrMrrJrKrrrr!r¿s(zEmu3TextConfig.__init__)r1r2r3r4r4r5r6r7r8Tr9r:r;NFFr<r=F)r"r#r$r%r&r'Úkeys_to_ignore_at_inferenceÚ default_thetar(Ústrr+r)rrr,rrrr!r-osxJìþýüûúùø ÷ ö õ ô óòñîíìr-c sheZdZdZdZdgZeedœZ    d de eBde eBd e e e fdBd e dBf‡fd d „ Z ‡Z S)Ú Emu3Configa• This is the configuration class to store the configuration of a [`Emu3Model`]. It is used to instantiate a emu3 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 [Emu3-community/Emu3-Chat-hf](https://huggingface.co/Emu3-community/Emu3-Chat-hf). Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PreTrainedConfig`] for more information. Args: vq_config (`Union[Dict, Emu3VQVAEConfig]`, *optional*): Emu3VQVAEConfig instance containing the configuration for the VQ-VAE model. text_config (`Union[Dict, Emu3TextConfig]``, *optional*): Emu3TextConfig instance containing the configuration for the language model. vocabulary_map (`dict`, *optional*): A dictionary containing the vocabulary map from the tokenizer. Used to obtain tokens from the image inputs. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie weight embeddings Úemu3r0)r/rNFrr/Úvocabulary_maprKc s–|durtƒ}n t|tƒrtdi|¤Ž}|durtƒ}n t|tƒr(tdi|¤Ž}||_||_||_|dur:| d¡nd|_||_ t ƒj di|¤ŽdS)Nzr) rÚ isinstanceÚdictr-rr/rSÚgetÚimage_token_idrKrr)rrr/rSrKrrrr!rs  zEmu3Config.__init__)NNNF)r"r#r$r%r&rNr-rÚ sub_configsrUr(r)rr,rrrr!rQís$ ûþýüûrQ)rQr-rN)Úconfiguration_utilsrÚmodeling_rope_utilsrrr-rQÚ__all__rrrr!Ús  Z~ 5