g fdZddlZddlmZddlmZejeZGddeZ dS)zMVP model configurationN)PretrainedConfig)loggingcpeZdZdZdZdgZdddZ dfd ZxZS) MvpConfiga This is the configuration class to store the configuration of a [`MvpModel`]. It is used to instantiate a MVP 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 MVP [RUCAIBox/mvp](https://huggingface.co/RUCAIBox/mvp) architecture. 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 50267): Vocabulary size of the MVP model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`MvpModel`]. d_model (`int`, *optional*, defaults to 1024): Dimensionality of the layers and the pooler layer. encoder_layers (`int`, *optional*, defaults to 12): Number of encoder layers. decoder_layers (`int`, *optional*, defaults to 12): Number of decoder layers. encoder_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. decoder_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer decoder. decoder_ffn_dim (`int`, *optional*, defaults to 4096): Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. encoder_ffn_dim (`int`, *optional*, defaults to 4096): Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. activation_function (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. activation_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for activations inside the fully connected layer. classifier_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for classifier. max_position_embeddings (`int`, *optional*, defaults to 1024): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). init_std (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. encoder_layerdrop (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. decoder_layerdrop (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. scale_embedding (`bool`, *optional*, defaults to `False`): Scale embeddings by diving by sqrt(d_model). use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). forced_eos_token_id (`int`, *optional*, defaults to 2): The id of the token to force as the last generated token when `max_length` is reached. Usually set to `eos_token_id`. use_prompt (`bool`, *optional*, defaults to `False`): Whether or not to use prompt. prompt_length (`int`, *optional*, defaults to 100): The length of prompt. prompt_mid_dim (`int`, *optional*, defaults to 800): Dimensionality of the "intermediate" layer in prompt. Example: ```python >>> from transformers import MvpConfig, MvpModel >>> # Initializing a MVP RUCAIBox/mvp style configuration >>> configuration = MvpConfig() >>> # Initializing a model (with random weights) from the RUCAIBox/mvp style configuration >>> model = MvpModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```mvppast_key_valuesencoder_attention_headsd_model)num_attention_heads hidden_size[ gelu皙?{Gz?FTrd c ||_||_| |_||_||_||_||_||_||_| |_ ||_ ||_ | |_ ||_ | |_| |_||_||_||_||_||_||_||_t/jd||||||d||jA|ddr-|j|_t9jd|jddSdSdS)N) pad_token_id bos_token_id eos_token_idis_encoder_decoderdecoder_start_token_idforced_eos_token_idforce_bos_token_to_be_generatedFz:Please make sure the config includes `forced_bos_token_id=zT` in future versions. The config can simply be saved and uploaded again to be fixed.) vocab_sizemax_position_embeddingsr encoder_ffn_dimencoder_layersr decoder_ffn_dimdecoder_layersdecoder_attention_headsdropoutattention_dropoutactivation_dropoutactivation_functioninit_stdencoder_layerdropdecoder_layerdropclassifier_dropout use_cachenum_hidden_layersscale_embedding use_prompt prompt_lengthprompt_mid_dimsuper__init__forced_bos_token_idgetrwarningswarn)selfr$r%r'r&r r)r(r*r0r1r.r r+r,r-r/r2r5r3rrrrr r!r6r7r8kwargs __class__s e/var/www/html/ai-engine/env/lib/python3.11/site-packages/transformers/models/mvp/configuration_mvp.pyr:zMvpConfig.__init__msm@%'>$ .,'>$.,'>$ !2"4#6   !2!2"4"!/.$*, %%%1#9 3        # + ;\^c0d0d +'+'8D $ MQTM^QQQ      , + + +)rrrrrrrrrrrrrrrrrFTrrrTrrFrr) __name__ __module__ __qualname____doc__ model_typekeys_to_ignore_at_inference attribute_mapr: __classcell__)rAs@rBrrsL L \J#4"5,EV_``M $ " "" ;GGGGGGGGGGrCr) rGr=configuration_utilsrutilsr get_loggerrDloggerrr#rCrBrPs333333  H % %ZZZZZ ZZZZZrC