§ ñ§g¥ãó^—dZddlmZddlmZeje¦«ZGd„de¦«ZdS)zOpenAI GPT configurationé)ÚPretrainedConfig)ÚloggingcóV‡—eZdZdZdZdddddœZ dˆfd„ ZˆxZS)ÚOpenAIGPTConfiga“ This is the configuration class to store the configuration of a [`OpenAIGPTModel`] or a [`TFOpenAIGPTModel`]. It is used to instantiate a GPT 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 [openai-community/openai-gpt](https://huggingface.co/openai-community/openai-gpt) architecture from OpenAI. 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 40478): Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`OpenAIGPTModel`] or [`TFOpenAIGPTModel`]. n_positions (`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). 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. afn (`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. resid_pdrop (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. embd_pdrop (`int`, *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 (`str`, *optional*, defaults to `"cls_index"`): Argument used when doing sequence summary, used in the models [`OpenAIGPTDoubleHeadsModel`] and [`OpenAIGPTDoubleHeadsModel`]. 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 [`OpenAIGPTDoubleHeadsModel`] and [`OpenAIGPTDoubleHeadsModel`]. Whether or not to add a projection after the vector extraction. summary_activation (`str`, *optional*): Argument used when doing sequence summary, used in the models [`OpenAIGPTDoubleHeadsModel`] and [`OpenAIGPTDoubleHeadsModel`]. 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 [`OpenAIGPTDoubleHeadsModel`] and [`OpenAIGPTDoubleHeadsModel`]. 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 [`OpenAIGPTDoubleHeadsModel`] and [`OpenAIGPTDoubleHeadsModel`]. The dropout ratio to be used after the projection and activation. Examples: ```python >>> from transformers import OpenAIGPTConfig, OpenAIGPTModel >>> # Initializing a GPT configuration >>> configuration = OpenAIGPTConfig() >>> # Initializing a model (with random weights) from the configuration >>> model = OpenAIGPTModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```z openai-gptÚ n_positionsÚn_embdÚn_headÚn_layer)Úmax_position_embeddingsÚ hidden_sizeÚnum_attention_headsÚnum_hidden_layerséžééé Úgeluçš™™™™™¹?çñh㈵øä>ç{®Gáz”?Ú cls_indexTNc ó•—||_||_||_||_||_||_||_||_| |_| |_ | |_ | |_ | |_ ||_ ||_||_t!¦«jdi|¤ŽdS)N©)Ú vocab_sizerrr r ÚafnÚ resid_pdropÚ embd_pdropÚ attn_pdropÚlayer_norm_epsilonÚinitializer_rangeÚ summary_typeÚsummary_use_projÚsummary_activationÚsummary_first_dropoutÚsummary_proj_to_labelsÚsuperÚ__init__)Úselfrrrr r rrrrrr r!r"r#r%r$ÚkwargsÚ __class__s €úk/var/www/html/ai-engine/env/lib/python3.11/site-packages/transformers/models/openai/configuration_openai.pyr'zOpenAIGPTConfig.__init__us¡ø€ð(%ˆŒØ&ˆÔ؈Œ ؈Œ ؈Œ ؈ŒØ&ˆÔØ$ˆŒØ$ˆŒØ"4ˆÔØ!2ˆÔØ(ˆÔØ 0ˆÔØ"4ˆÔØ%:ˆÔ"Ø&<ˆÔ#؉ŒÔÐ"Ð"˜6Ð"Ð"Ð"Ð"Ð"ó)rrrrrrrrrrrrTNTr)Ú__name__Ú __module__Ú __qualname__Ú__doc__Ú model_typeÚ attribute_mapr'Ú __classcell__)r*s@r+rrs›ø€€€€€ðQ ðQ ðf€Jà#0ØØ'Ø&ð ð€MðØØØØØ ØØØØØØ ØØØ#Ø!ð#$#ð$#ð$#ð$#ð$#ð$#ð$#ð$#ð$#ð$#r,rN) r0Úconfiguration_utilsrÚutilsrÚ get_loggerr-Úloggerrrr,r+úr8s„ðð Ðà3Ð3Ð3Ð3Ð3Ð3ØÐÐÐÐÐð ˆÔ ˜HÑ %Ô %€ð@#ð@#ð@#ð@#ð@#Ð&ñ@#ô@#ð@#ð@#ð@#r,