o ZŽhó ã@sHdZddlZddlmZddlmZe e¡ZGdd„deƒZ dgZ dS)zMVP model configurationéNé)ÚPretrainedConfig)ÚloggingcsneZdZdZdZdgZdddœZ                d‡fdd„ Z‡ZS)Ú 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 ```ZmvpZpast_key_valuesÚencoder_attention_headsÚd_model)Znum_attention_headsZ hidden_sizeé[Äéé ééçÚgeluçš™™™™™¹?ç{®Gáz”?FTéréédé c sè||_||_| |_||_||_||_||_||_||_| |_ ||_ ||_ | |_ ||_ | |_| |_||_||_||_||_||_||_||_tƒjd||||||dœ|¤Ž|jdurp| dd¡rr|j|_t d|j›d¡dSdSdS)N)Ú pad_token_idÚ bos_token_idÚ eos_token_idÚis_encoder_decoderÚdecoder_start_token_idÚforced_eos_token_idZforce_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.©)Ú vocab_sizeÚmax_position_embeddingsrÚencoder_ffn_dimÚencoder_layersrÚdecoder_ffn_dimÚdecoder_layersÚdecoder_attention_headsÚdropoutÚattention_dropoutÚactivation_dropoutÚactivation_functionÚinit_stdÚencoder_layerdropÚdecoder_layerdropÚclassifier_dropoutÚ use_cacheZnum_hidden_layersÚscale_embeddingÚ use_promptÚ prompt_lengthÚprompt_mid_dimÚsuperÚ__init__Zforced_bos_token_idÚgetrÚwarningsÚwarn)Úselfrrrrrr!r r"r(r)r&rr#r$r%r'r*r,r+rrrrrrr-r.r/Úkwargs©Ú __class__rúX/var/www/auris/lib/python3.10/site-packages/transformers/models/mvp/configuration_mvp.pyr1msN úù  ÿþzMvpConfig.__init__)rr r r r r r r r r rr rr r rr FTrrrTrrFrr) Ú__name__Ú __module__Ú __qualname__Ú__doc__Z model_typeZkeys_to_ignore_at_inferenceZ attribute_mapr1Ú __classcell__rrr7r9rsDN ãr) r=r3Zconfiguration_utilsrÚutilsrZ get_loggerr:ÚloggerrÚ__all__rrrr9Ús