`iD?ddlmZmZmZddlmZddlmZGddeZGddeZ Gd d eZ gd Z d S) )AnyOptionalUnion)PretrainedConfig)rope_config_validationceZdZdZdZdZddddddddgd d dgd d d fdededededededededeededeededede ffd Z xZ S)Emu3VQVAEConfiga 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 ``` emu3_vqgan vq_configiFr)rr rirg codebook_size embed_dimlatent_channels double_latent in_channels out_channelstemporal_downsample_factor base_channelschannel_multipliernum_res_blocksattn_resolutions hidden_sizenum_attention_headsattention_dropoutc tjdi|||_||_||_||_||_||_||_||_ | |_ | |_ | |_ | |_ | |_||_dS)N)super__init__rrrrrrrrrrrrrr)selfrrrrrrrrrrrrrrkwargs __class__s /home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/transformers/models/emu3/configuration_emu3.pyr"zEmu3VQVAEConfig.__init__Ms$ ""6"""*".*&(*D'*"4, 0&#6 !2) __name__ __module__ __qualname____doc__ model_typebase_config_keyintboollistfloatr" __classcell__r%s@r&r r s0 0 dJ!O# #*+ (4 '(c#$#&!3!3!3!3 !3  !3  !3!3%(!3!3!I!3!3s)!3!3!!3!!3!3!3!3!3!3!3!3!3!3r'r c%eZdZdZdZdZdgZ d)dedededededeede dede de d ed!ed"ed#e d$e d%ee e e fd&e d'e f$fd( ZxZS)*Emu3TextConfiga 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. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie weight embeddings rope_theta (`float`, *optional*, defaults to 1000000.0): 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 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. ```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 ```emu3_text_model text_configpast_key_values.8 silu$h㈵>T[P)Q*QF.AN皙?{Gz? vocab_sizerintermediate_sizenum_hidden_layersrnum_key_value_heads hidden_actmax_position_embeddings rms_norm_eps use_cache pad_token_id bos_token_id eos_token_idtie_word_embeddings rope_theta rope_scalingrinitializer_rangec B||_||_||_||_||_||_||_||_| |_| |_ ||_ ||_ ||_ ||_ ||_t|||_t#jd| | | |d|dS)N)rOrPrQrRr )rGrLrrHrIrrJrKrMrNrSrTmlp_biasattention_biasrUrrr!r")r#rGrrHrIrrJrKrLrMrNrOrPrQrRrSrTrWrXrrUr$r%s r&r"zEmu3TextConfig.__init__s0%'>$&!2!2#6 #6 $("$(  ,!2t$$$!2 %%% 3         r')r9r:r;r<r<r=r>r?r@TrArBrCFrDNFFrErF)r(r)r*r+r,r-keys_to_ignore_at_inferencer.rstrr1r/dictrr"r2r3s@r&r5r5qsek k Z#J#O#4"5!!&!##%-. '+""""$)%15#&#'+1 1 1 1  1  1 ! 1 &c]1 1 "%1 1 1 1 1 1 "1 !1 "tCH~.#1 (!)1 *!+1 1 1 1 1 1 1 1 1 1 r'r5c eZdZdZdZdgZeedZ d de e efde e efde e e e fffd Z xZS) Emu3Configa! This is the configuration class to store the configuration of a [`Emu3Model`]. It is used to instantiate a emu3 model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the [Emu3-community/Emu3-Chat-hf](https://huggingface.co/Emu3-community/Emu3-Chat-hf). Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vq_config (`Union[Dict, Emu3VQVAEConfig]`, *optional*): Emu3VQVAEConfig instance containing the configuration for the VQ-VAE model. text_config (`Union[Dict, Emu3TextConfig]``, *optional*): Emu3TextConfig instance containing the configuration for the language model. vocabulary_map (`dict`, *optional*): A dictionary containing the vocabulary map from the tokenizer. Used to obtain tokens from the image inputs. emu3r8)r7r Nr r7vocabulary_mapc h|t}n!t|tr tdi|}|t}n!t|tr tdi|}||_||_||_||dnd|_tj di|dS)Nzr ) r isinstancer[r5r r7r_getimage_token_idr!r")r#r r7r_r$r%s r&r"zEmu3Config.__init__/s  '))II  4 ( ( 5'44)44I  (**KK  T * * 8(77;77K"&,?M?Yn00;;;_c""6"""""r')NNN)r(r)r*r+r,rYr5r sub_configsrr[rr.r"r2r3s@r&r]r]s&J#4"5"0OOK373737 ##./#4/0#!c3h0 ##########r'r])r]r5r N) typingrrrconfiguration_utilsrmodeling_rope_utilsrr r5r]__all__r r'r&ris"('''''''''333333999999W3W3W3W3W3&W3W3W3tc c c c c %c c c L.#.#.#.#.#!.#.#.#b > = =r'