`i/dZddlmZddlmZddlmZmZddlm Z m Z m Z ddl m Z ddlmZmZdd lmZejeZGd d e ZGd d eZd d gZdS)zOpenAI GPT-2 configuration) OrderedDict)Mapping)AnyOptional)PreTrainedTokenizer TensorTypeis_torch_available)PretrainedConfig)OnnxConfigWithPast PatchingSpec)loggingcjeZdZdZdZdgZdddddZ dfd ZxZS) GPT2ConfigaK This is the configuration class to store the configuration of a [`GPT2Model`] or a [`TFGPT2Model`]. 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`] or [`TFGPT2Model`]. 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`] and [`TFGPT2DoubleHeadsModel`]. 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`] and [`TFGPT2DoubleHeadsModel`]. 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`] and [`TFGPT2DoubleHeadsModel`]. 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`] and [`TFGPT2DoubleHeadsModel`]. 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. 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. 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 ```gpt2past_key_valuesn_embd n_positionsn_headn_layer) hidden_sizemax_position_embeddingsnum_attention_headsnum_hidden_layersQ Ngelu_new皙?h㈵>{Gz? cls_indexTPFc ||_||_||_||_||_||_||_||_| |_| |_ | |_ | |_ | |_ ||_ ||_||_||_||_||_||_||_||_||_t/jd||d|dS)N) bos_token_id eos_token_id) vocab_sizerrrrn_inneractivation_function resid_pdrop embd_pdrop attn_pdroplayer_norm_epsiloninitializer_range summary_typesummary_use_projsummary_activationsummary_first_dropoutsummary_proj_to_labelsscale_attn_weights use_cachescale_attn_by_inverse_layer_idxreorder_and_upcast_attnr&r'super__init__)selfr)rrrrr*r+r,r-r.r/r0r1r2r3r5r4r6r7r&r'r8r9kwargs __class__s /home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/transformers/models/gpt2/configuration_gpt2.pyr;zGPT2Config.__init__s6%&    #6 &$$"4!2( 0"4%:"&<#"4"/N,'>$((XlXXQWXXXXX)rrrrrNrr r r r!r"r#TNTr TTr$r$FF) __name__ __module__ __qualname____doc__ model_typekeys_to_ignore_at_inference attribute_mapr; __classcell__r>s@r?rrs_ _ BJ#4"5#0'& M& #!(- %14Y4Y4Y4Y4Y4Y4Y4Y4Y4Yr@rc*eZdZ ddededeeedeffd Z e d e ee e efffd Z e d e fd Ze d e fd Z ddede de dedeed e eeff fd Ze d e fdZxZS)GPT2OnnxConfigdefaultNFconfigtaskpatching_specsuse_pastct||||t|jddsd|j_dSdS)N)rNrOrP pad_token_idr)r:r;getattr_configrR)r<rMrNrOrPr>s r?r;zGPT2OnnxConfig.__init__sW d>T\]]]t|^T:: *()DL % % % * *r@returnctddddi}|jr ||dddd|d<nddd|d<|S) N input_idsbatchsequence)rinputs) directionzpast_sequence + sequenceattention_mask)rrPfill_with_past_key_values_)r< common_inputss r?r[zGPT2OnnxConfig.inputssp#[g*2M2M$NOO = J  + +MX + N N N29>X.Y.YM* + +29j.I.IM* +r@c|jjSN)rTrr<s r? num_layerszGPT2OnnxConfig.num_layerss |##r@c|jjSra)rTrrbs r?rz"GPT2OnnxConfig.num_attention_headss |""r@ tokenizer batch_size seq_lengthis_pair frameworkc> tt||||||}td|di}|jrwt st dddl |dj\}} | dz} ||j | |j j |j zf fdt|j D|d<|d|d<|jrE|dj} |d || | gd |d<|S) N)rgrhrirjrWzACannot generate dummy past_keys inputs without PyTorch installed.rcdg|],}f-Sr()zeros).0_ past_shapetorchs r? z8GPT2OnnxConfig.generate_dummy_inputs..sC555KLU[[,,ekk*.E.EF555r@rr])dtyperZ)dim)r:r generate_dummy_inputsrrPr ValueErrorrrshaperrTrrangercrtcatones)r<rfrgrhrirjr_ordered_inputsrXseqlenpast_key_values_length mask_dtyperqrrr>s @@r?rvz$GPT2OnnxConfig.generate_dummy_inputssw0$77MM *W`iN  %k=3M%NOO = %''  !deee -k : @ v)/!&,*L,0HH  55555PUVZVePfPf55501,99I+J'( = '(89?J/4yy 015::eE[cm:3n3nouv0900N+ ,r@cdS)N r(rbs r?default_onnx_opsetz!GPT2OnnxConfig.default_onnx_opset srr@)rLNF)rereFN)rArBrCr strrlistr boolr;propertyrintr[rcrrr rrvrrHrIs@r?rKrKs7; * *  * *!l!34 *  * * * * * *WS#X%6 67X$C$$$X$#S###X# *. **&** *  * J' * c ******XCXr@rKN)rD collectionsrcollections.abcrtypingrrrr r configuration_utilsr onnxr r utilsr get_loggerrAloggerrrK__all__r(r@r?rs8 ! ############ CCCCCCCCCC33333344444444  H % %_Y_Y_Y_Y_Y!_Y_Y_YDNNNNN'NNNb ) *r@