U ,È-e¤ã@sBdZddlmZddlmZe e¡ZddiZGdd„deƒZ dS) z OpenAI GPT configurationé)ÚPretrainedConfig)Úloggingú openai-gptz:https://huggingface.co/openai-gpt/resolve/main/config.jsoncs4eZdZdZdZdddddœZd‡fdd„ Z‡ZS)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-gpt](https://huggingface.co/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-5): 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. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). 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 ```rÚ n_positionsÚn_embdÚn_headÚn_layer)Zmax_position_embeddingsZ hidden_sizeZnum_attention_headsZnum_hidden_layerséžééé Úgeluçš™™™™™¹?çñh㈵øä>ç{®Gáz”?Ú cls_indexTNc sr||_||_||_||_||_||_||_||_| |_| |_ | |_ | |_ | |_ ||_ ||_||_tƒjf|ŽdS)N)Ú vocab_sizerrr rÚafnÚ resid_pdropÚ embd_pdropÚ attn_pdropÚlayer_norm_epsilonÚinitializer_rangeÚ summary_typeÚsummary_use_projÚsummary_activationÚsummary_first_dropoutÚsummary_proj_to_labelsÚsuperÚ__init__)Úselfrrrr rrrrrrrrrrrrÚkwargs©Ú __class__©úp/var/www/html/Darija-Ai-Train/env/lib/python3.8/site-packages/transformers/models/openai/configuration_openai.pyr ys"zOpenAIGPTConfig.__init__)r r r r r rrrrrrrTNTr)Ú__name__Ú __module__Ú __qualname__Ú__doc__Z model_typeZ attribute_mapr Ú __classcell__r%r%r#r&rs0Uü ïrN) r*Zconfiguration_utilsrÚutilsrZ get_loggerr'ÚloggerZ(OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAPrr%r%r%r&Ús