o hC@sdZddlmZddlmZddlmZddlmZddl m Z e e Z dd d d d d ddZGdddeZGdddeZdS)z DistilBERT model configuration) OrderedDict)Mapping)PretrainedConfig) OnnxConfig)loggingzGhttps://huggingface.co/distilbert-base-uncased/resolve/main/config.jsonzWhttps://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.jsonzEhttps://huggingface.co/distilbert-base-cased/resolve/main/config.jsonzUhttps://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.jsonzLhttps://huggingface.co/distilbert-base-german-cased/resolve/main/config.jsonzRhttps://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.jsonz_https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json)zdistilbert-base-uncasedz'distilbert-base-uncased-distilled-squadzdistilbert-base-casedz%distilbert-base-cased-distilled-squadzdistilbert-base-german-casedz"distilbert-base-multilingual-casedz/distilbert-base-uncased-finetuned-sst-2-englishcsNeZdZdZdZddddZ         dfdd ZZS)DistilBertConfiga This is the configuration class to store the configuration of a [`DistilBertModel`] or a [`TFDistilBertModel`]. It is used to instantiate a DistilBERT 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 DistilBERT [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) 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 30522): Vocabulary size of the DistilBERT model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`DistilBertModel`] or [`TFDistilBertModel`]. max_position_embeddings (`int`, *optional*, defaults to 512): 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). sinusoidal_pos_embds (`boolean`, *optional*, defaults to `False`): Whether to use sinusoidal positional embeddings. n_layers (`int`, *optional*, defaults to 6): Number of hidden layers in the Transformer encoder. n_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. dim (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. hidden_dim (`int`, *optional*, defaults to 3072): The size of the "intermediate" (often named feed-forward) layer in the Transformer encoder. 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 0.1): The dropout ratio for the attention probabilities. activation (`str` or `Callable`, *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. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. qa_dropout (`float`, *optional*, defaults to 0.1): The dropout probabilities used in the question answering model [`DistilBertForQuestionAnswering`]. seq_classif_dropout (`float`, *optional*, defaults to 0.2): The dropout probabilities used in the sequence classification and the multiple choice model [`DistilBertForSequenceClassification`]. Examples: ```python >>> from transformers import DistilBertConfig, DistilBertModel >>> # Initializing a DistilBERT configuration >>> configuration = DistilBertConfig() >>> # Initializing a model (with random weights) from the configuration >>> model = DistilBertModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` distilbertdimn_headsn_layers) hidden_sizenum_attention_headsnum_hidden_layers:wF  皙?gelu{Gz?皙?rc sl||_||_||_||_||_||_||_||_| |_| |_ | |_ | |_ | |_ t jdi|d|idS)N pad_token_id) vocab_sizemax_position_embeddingssinusoidal_pos_embdsr r r hidden_dimdropoutattention_dropout activationinitializer_range qa_dropoutseq_classif_dropoutsuper__init__)selfrrrr r r rr r!r"r#r$r%rkwargs __class__rm/var/www/html/ai/venv/lib/python3.10/site-packages/transformers/models/distilbert/configuration_distilbert.pyr'mszDistilBertConfig.__init__)rrFrrrrrrrrrrr)__name__ __module__ __qualname____doc__ model_type attribute_mapr' __classcell__rrr*r,r-s,8rc@s.eZdZedeeeeefffddZdS)DistilBertOnnxConfigreturncCs6|jdkr dddd}nddd}td|fd|fgS) Nzmultiple-choicebatchchoicesequence)r)rr9 input_idsattention_mask)taskr)r( dynamic_axisrrr,inputss  zDistilBertOnnxConfig.inputsN)r-r.r/propertyrstrintr?rrrr,r4s$r4N)r0 collectionsrtypingrconfiguration_utilsronnxrutilsr get_loggerr-logger(DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAPrr4rrrr,s"      b