o ¾e¦io#ã@s@dZddlmZddlmZe e¡ZGdd„deƒZdgZ dS)zOpenAI GPT-2 configurationé)ÚPreTrainedConfig)ÚloggingcsneZdZdZdZdgZdddddœZ                  d‡fdd„ Z‡ZS)Ú GPT2Configa This is the configuration class to store the configuration of a [`GPT2Model`]. It is used to instantiate a GPT-2 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 GPT-2 [openai-community/gpt2](https://huggingface.co/openai-community/gpt2) 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 50257): Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`GPT2Model`]. n_positions (`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). n_embd (`int`, *optional*, defaults to 768): Dimensionality of the embeddings and hidden states. n_layer (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. n_head (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. n_inner (`int`, *optional*): Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd activation_function (`str`, *optional*, defaults to `"gelu_new"`): Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new"]`. resid_pdrop (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. embd_pdrop (`float`, *optional*, defaults to 0.1): The dropout ratio for the embeddings. attn_pdrop (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention. layer_norm_epsilon (`float`, *optional*, defaults to 1e-05): The epsilon to use in the layer normalization layers. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. summary_type (`string`, *optional*, defaults to `"cls_index"`): Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`]. Has to be one of the following options: - `"last"`: Take the last token hidden state (like XLNet). - `"first"`: Take the first token hidden state (like BERT). - `"mean"`: Take the mean of all tokens hidden states. - `"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2). - `"attn"`: Not implemented now, use multi-head attention. summary_use_proj (`bool`, *optional*, defaults to `True`): Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`]. Whether or not to add a projection after the vector extraction. summary_activation (`str`, *optional*): Argument used when doing sequence summary. Used in for the multiple choice head in [`GPT2DoubleHeadsModel`]. Pass `"tanh"` for a tanh activation to the output, any other value will result in no activation. summary_proj_to_labels (`bool`, *optional*, defaults to `True`): Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`]. Whether the projection outputs should have `config.num_labels` or `config.hidden_size` classes. summary_first_dropout (`float`, *optional*, defaults to 0.1): Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`]. The dropout ratio to be used after the projection and activation. scale_attn_weights (`bool`, *optional*, defaults to `True`): Scale attention weights by dividing by sqrt(hidden_size).. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). bos_token_id (`int`, *optional*, defaults to 50256): Id of the beginning of sentence token in the vocabulary. eos_token_id (`int`, *optional*, defaults to 50256): Id of the end of sentence token in the vocabulary. pad_token_id (`int`, *optional*): Padding token id. scale_attn_by_inverse_layer_idx (`bool`, *optional*, defaults to `False`): Whether to additionally scale attention weights by `1 / layer_idx + 1`. reorder_and_upcast_attn (`bool`, *optional*, defaults to `False`): Whether to scale keys (K) prior to computing attention (dot-product) and upcast attention dot-product/softmax to float() when training with mixed precision. add_cross_attention (`bool`, *optional*, defaults to `False`): Whether cross-attention layers should be added to the model. tie_word_embeddings (`bool`, *optional*, defaults to `True`): Whether to tie weight embeddings Example: ```python >>> from transformers import GPT2Config, GPT2Model >>> # Initializing a GPT2 configuration >>> configuration = GPT2Config() >>> # Initializing a model (with random weights) from the configuration >>> model = GPT2Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```Úgpt2Úpast_key_valuesÚn_embdÚ n_positionsÚn_headÚn_layer)Ú hidden_sizeÚmax_position_embeddingsÚnum_attention_headsÚnum_hidden_layerséQÄééé NÚgelu_newçš™™™™™¹?çñh㈵øä>ç{®Gáz”?Ú cls_indexTéPÄFc s²||_||_||_||_||_||_||_||_||_||_ | |_ | |_ | |_ | |_ | |_||_||_||_||_||_||_||_||_||_||_||_tƒjdi|¤ŽdS)N©)Úadd_cross_attentionÚtie_word_embeddingsÚ vocab_sizerrr r Ún_innerÚactivation_functionÚ resid_pdropÚ embd_pdropÚ attn_pdropÚlayer_norm_epsilonÚinitializer_rangeÚ summary_typeÚsummary_use_projÚsummary_activationÚsummary_first_dropoutÚsummary_proj_to_labelsÚscale_attn_weightsÚ use_cacheÚscale_attn_by_inverse_layer_idxÚreorder_and_upcast_attnÚ bos_token_idÚ eos_token_idÚ pad_token_idÚsuperÚ__init__)Úselfrrrr r rrrr r!r"r#r$r%r&r(r'r)r*r-r.r/r+r,rrÚkwargs©Ú __class__rúi/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/transformers/models/gpt2/configuration_gpt2.pyr1…s6zGPT2Config.__init__)rrrrrNrrrrrrrTNTrTTrrNFFFT) Ú__name__Ú __module__Ú __qualname__Ú__doc__Ú model_typeÚkeys_to_ignore_at_inferenceÚ attribute_mapr1Ú __classcell__rrr4r6rsHcü årN) r:Úconfiguration_utilsrÚutilsrÚ get_loggerr7ÚloggerrÚ__all__rrrr6Ús    +