
    fThM                     `    S r SSKJr  SSKJr  \R
                  " \5      r " S S\5      rS/r	g)zPEGASUS model configuration   )PretrainedConfig)loggingc                      ^  \ rS rSrSrSrS/rSSS.r                       SU 4S jjr\	S	\
4S
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4S j5       rSrU =r$ )PegasusConfig   a  
This is the configuration class to store the configuration of a [`PegasusModel`]. It is used to instantiate an
PEGASUS 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 PEGASUS
[google/pegasus-large](https://huggingface.co/google/pegasus-large) 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 50265):
        Vocabulary size of the PEGASUS model. Defines the number of different tokens that can be represented by the
        `inputs_ids` passed when calling [`PegasusModel`] or [`TFPegasusModel`].
    d_model (`int`, *optional*, defaults to 1024):
        Dimensionality of the layers and the pooler layer.
    encoder_layers (`int`, *optional*, defaults to 12):
        Number of encoder layers.
    decoder_layers (`int`, *optional*, defaults to 12):
        Number of decoder layers.
    encoder_attention_heads (`int`, *optional*, defaults to 16):
        Number of attention heads for each attention layer in the Transformer encoder.
    decoder_attention_heads (`int`, *optional*, defaults to 16):
        Number of attention heads for each attention layer in the Transformer decoder.
    decoder_ffn_dim (`int`, *optional*, defaults to 4096):
        Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
    encoder_ffn_dim (`int`, *optional*, defaults to 4096):
        Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
    activation_function (`str` or `function`, *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.
    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 0.0):
        The dropout ratio for the attention probabilities.
    activation_dropout (`float`, *optional*, defaults to 0.0):
        The dropout ratio for activations inside the fully connected layer.
    max_position_embeddings (`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).
    init_std (`float`, *optional*, defaults to 0.02):
        The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
    encoder_layerdrop (`float`, *optional*, defaults to 0.0):
        The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
        for more details.
    decoder_layerdrop (`float`, *optional*, defaults to 0.0):
        The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
        for more details.
    scale_embedding (`bool`, *optional*, defaults to `False`):
        Scale embeddings by diving by sqrt(d_model).
    use_cache (`bool`, *optional*, defaults to `True`):
        Whether or not the model should return the last key/values attentions (not used by all models)
    forced_eos_token_id (`int`, *optional*, defaults to 1):
        The id of the token to force as the last generated token when `max_length` is reached. Usually set to
        `eos_token_id`.

Example:

```python
>>> from transformers import PegasusConfig, PegasusModel

>>> # Initializing a PEGASUS google/pegasus-large style configuration
>>> configuration = PegasusConfig()

>>> # Initializing a model (with random weights) from the google/pegasus-large style configuration
>>> model = PegasusModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```pegasuspast_key_valuesencoder_attention_headsd_model)num_attention_headshidden_sizec           	        > Xl         X l        Xl        X@l        X0l        XPl        Xpl        X`l        Xl        Xl	        UU l
        UU l        Xl        UU l        Xl        Xl        Xl        X0l        UU l        [&        TU ]P  " SUUUUUS.UD6  g )N)pad_token_ideos_token_idis_encoder_decoderdecoder_start_token_idforced_eos_token_id )
vocab_sizemax_position_embeddingsr   encoder_ffn_dimencoder_layersr
   decoder_ffn_dimdecoder_layersdecoder_attention_headsdropoutattention_dropoutactivation_dropoutactivation_functioninit_stdencoder_layerdropdecoder_layerdrop	use_cachenum_hidden_layersscale_embeddingsuper__init__)selfr   r   r   r   r
   r   r   r   r!   r"   r#   r   r   r   r   r   r   r    r   r%   r   r   r   kwargs	__class__s                            i/var/www/auris/envauris/lib/python3.13/site-packages/transformers/models/pegasus/configuration_pegasus.pyr'   PegasusConfig.__init__d   s    6 %'>$.,'>$.,'>$!2"4#6  !2!2"!/. 	
%%1#9 3	
 	
    returnc                     U R                   $ N)r
   r(   s    r+   r   !PegasusConfig.num_attention_heads   s    +++r-   c                     U R                   $ r0   )r   r1   s    r+   r   PegasusConfig.hidden_size   s    ||r-   )r   r   r   r   r   r   r"   r   r   r
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