% &h>~ddlmZmZmZmZddlmZddlmZGddeZ GddeZ Gd d eZ gd Z y ) )DictListOptionalUnion)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)rrrirg codebook_size embed_dimlatent_channels double_latent in_channels out_channelstemporal_downsample_factor base_channelschannel_multipliernum_res_blocksattn_resolutions hidden_sizenum_attention_headsattention_dropoutc t|di|||_||_||_||_||_||_||_||_ | |_ | |_ | |_ | |_ | |_||_yN)super__init__rrrrrrrrrrrrrr)selfrrrrrrrrrrrrrrkwargs __class__s /var/www/pru.catia.catastroantioquia-mas.com/valormas/lib/python3.12/site-packages/transformers/models/emu3/configuration_emu3.pyr$zEmu3VQVAEConfig.__init__Ms$ "6"*".*&(*D'*"4, 0&#6 !2) __name__ __module__ __qualname____doc__ model_typebase_config_keyintboolrfloatr$ __classcell__r's@r(r r s0 dJ!O# #*+ (4'(c#$#&!3!3!3 !3  !3  !3!3%(!3!3!I!3!3s)!3!3!!3!!3!3r)r c%eZdZdZdZdZdgZ ddedededed ed eed e d ed e de dededede de dede de f$fd Z xZ S)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 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 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 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 $||_||_||_||_||_||_||_||_| |_| |_ ||_ ||_ ||_ ||_ ||_t|||_t#|Hd| | | |d|y)N)rBrCrDrEr")r:r?rr;r<rr=r>r@rArFrGmlp_biasattention_biasrHr rr#r$)r%r:rr;r<rr=r>r?r@rArBrCrDrErFrGrJrKrrHr&r's r(r$zEmu3TextConfig.__init__s0%'>$&!2!2#6 #6 $("$(  ,!2t$!2  %%% 3    r))i.ii8 rLsilui$gh㈵>Ti[Pi)Qi*QFg.ANFFg?g{Gz?)r*r+r,r-r.r/keys_to_ignore_at_inferencer0rstrr2r1r$r3r4s@r(r6r6qsk Z#J#O#4"5!!&!##%-. '+""""$)%!%#&#'+1 1 1  1  1 ! 1 &c]1 1 "%1 1 1 1 1 1 "1 !1 "#1 (!)1 *!+1 1 r)r6c jeZdZdZdZdgZeedZ d de e efde e efde e e fffd Z xZ S) 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. emu3r9)r8r r r8vocabulary_mapc | t}nt|tr tdi|}| t}nt|tr tdi|}||_||_||_t| di|yr!) r isinstancedictr6r r8rTr#r$)r%r r8rTr&r's r(r$zEmu3Config.__init__/sv  ')I  4 ('4)4I  (*K  T *(7;7K"&, "6"r))NNN)r*r+r,r-r.rOr6r sub_configsrrr0r$r3r4s@r(rRrRso&J#4"5"0OK3737)- #./#4/0#S#X ##r)rR)rRr6r N) typingrrrrconfiguration_utilsrmodeling_rope_utilsr r r6rR__all__r"r)r(r]sH"/.39W3&W3tc %c L-#!-#` >r)