qi(`dZddlmZddlmZej eZGddeZdgZ y)zXLM configuration)PreTrainedConfig)loggingc|eZdZdZdZddddddd d Z d fd ZxZS) XLMConfiga This is the configuration class to store the configuration of a [`XLMModel`]. It is used to instantiate a XLM 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 [FacebookAI/xlm-mlm-en-2048](https://huggingface.co/FacebookAI/xlm-mlm-en-2048) 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 30145): Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`XLMModel`]. emb_dim (`int`, *optional*, defaults to 2048): Dimensionality of the encoder layers and the pooler layer. n_layer (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. n_head (`int`, *optional*, defaults to 16): Number of attention heads for each attention 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 probability for the attention mechanism gelu_activation (`bool`, *optional*, defaults to `True`): Whether or not to use *gelu* for the activations instead of *relu*. sinusoidal_embeddings (`bool`, *optional*, defaults to `False`): Whether or not to use sinusoidal positional embeddings instead of absolute positional embeddings. causal (`bool`, *optional*, defaults to `False`): Whether or not the model should behave in a causal manner. Causal models use a triangular attention mask in order to only attend to the left-side context instead if a bidirectional context. asm (`bool`, *optional*, defaults to `False`): Whether or not to use an adaptive log softmax projection layer instead of a linear layer for the prediction layer. n_langs (`int`, *optional*, defaults to 1): The number of languages the model handles. Set to 1 for monolingual models. use_lang_emb (`bool`, *optional*, defaults to `True`) Whether to use language embeddings. Some models use additional language embeddings, see [the multilingual models page](http://huggingface.co/transformers/multilingual.html#xlm-language-embeddings) for information on how to use them. 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). embed_init_std (`float`, *optional*, defaults to 2048^-0.5): The standard deviation of the truncated_normal_initializer for initializing the embedding matrices. init_std (`int`, *optional*, defaults to 50257): The standard deviation of the truncated_normal_initializer for initializing all weight matrices except the embedding matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. bos_index (`int`, *optional*, defaults to 0): The index of the beginning of sentence token in the vocabulary. eos_index (`int`, *optional*, defaults to 1): The index of the end of sentence token in the vocabulary. pad_index (`int`, *optional*, defaults to 2): The index of the padding token in the vocabulary. unk_index (`int`, *optional*, defaults to 3): The index of the unknown token in the vocabulary. mask_index (`int`, *optional*, defaults to 5): The index of the masking token in the vocabulary. is_encoder(`bool`, *optional*, defaults to `True`): Whether or not the initialized model should be a transformer encoder or decoder as seen in Vaswani et al. summary_type (`string`, *optional*, defaults to "first"): Argument used when doing sequence summary. Used in the sequence classification and multiple choice models. 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 sequence classification and multiple choice models. Whether or not to add a projection after the vector extraction. summary_activation (`str`, *optional*): Argument used when doing sequence summary. Used in the sequence classification and multiple choice models. 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`): Used in the sequence classification and multiple choice models. Whether the projection outputs should have `config.num_labels` or `config.hidden_size` classes. summary_first_dropout (`float`, *optional*, defaults to 0.1): Used in the sequence classification and multiple choice models. The dropout ratio to be used after the projection and activation. start_n_top (`int`, *optional*, defaults to 5): Used in the SQuAD evaluation script. end_n_top (`int`, *optional*, defaults to 5): Used in the SQuAD evaluation script. mask_token_id (`int`, *optional*, defaults to 0): Model agnostic parameter to identify masked tokens when generating text in an MLM context. lang_id (`int`, *optional*, defaults to 1): The ID of the language used by the model. This parameter is used when generating text in a given language. Examples: ```python >>> from transformers import XLMConfig, XLMModel >>> # Initializing a XLM configuration >>> configuration = XLMConfig() >>> # Initializing a model (with random weights) from the configuration >>> model = XLMModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```xlmemb_dimn_headsn_layers vocab_size bos_token_id eos_token_id pad_token_id) hidden_sizenum_attention_headsnum_hidden_layersn_words bos_index eos_index pad_indexc! ||_||_||_||_||_||_||_||_| |_| |_ | |_ | |_ ||_ ||_ ||_||_| |_||_||_||_||_||_||_||_||_||_||_||_||_||_||_| |_d|!vr |!d|_ tC"|di|!y)zConstructs XLMConfig.rN)#r rr r dropoutattention_dropoutgelu_activationsinusoidal_embeddingscausalasmn_langs use_lang_emblayer_norm_eps unk_index mask_index is_encodermax_position_embeddingsembed_init_stdinit_std summary_typesummary_use_projsummary_activationsummary_proj_to_labelssummary_first_dropout start_n_top end_n_top mask_token_idlang_idrr r tie_word_embeddingsrsuper__init__)#selfr rr r rrrrrrrrr$r%r r&r!r"r#r'r(r)r*r+r,r-r.r/rr r r0kwargs __class__s# [/opt/pipecat/venv/lib/python3.12/site-packages/transformers/models/xlm/configuration_xlm.pyr2zXLMConfig.__init__sJ%     !2.%:"  (,"$$'>$,  ( 0"4&<#%:"&"* (((#6  !),DL "6") iui 皙?r:TFFFTig;f?g-q=g{Gz?rTfirstTNTr:r<r<r>r>r;T)__name__ __module__ __qualname____doc__ model_type attribute_mapr2 __classcell__)r5s@r6rrsm ^J ('###M#  #!#! 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