o
    ei                     @   s@   d Z ddlmZ ddlmZ eeZG dd deZdgZ	dS )zMarian model configuration   )PreTrainedConfig)loggingc                       sn   e Zd ZdZdZdgZdddZ					
			
																						d fdd	Z  ZS )MarianConfiga  
    This is the configuration class to store the configuration of a [`MarianModel`]. It is used to instantiate an
    Marian 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 Marian
    [Helsinki-NLP/opus-mt-en-de](https://huggingface.co/Helsinki-NLP/opus-mt-en-de) 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 58101):
            Vocabulary size of the Marian model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`MarianModel`].
        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://huggingface.co/papers/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://huggingface.co/papers/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 0):
            The id of the token to force as the last generated token when `max_length` is reached. Usually set to
            `eos_token_id`.

    Examples:

    ```python
    >>> from transformers import MarianModel, MarianConfig

    >>> # Initializing a Marian Helsinki-NLP/opus-mt-en-de style configuration
    >>> configuration = MarianConfig()

    >>> # Initializing a model from the Helsinki-NLP/opus-mt-en-de style configuration
    >>> model = MarianModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```marianpast_key_valuesencoder_attention_headsd_model)num_attention_headshidden_size  N                    Tgelu皙?{Gz?  F    c                    s   || _ || _|| _|p|| _|| _|| _|| _|| _|| _|| _	|| _
|	| _|| _|| _|| _|| _|| _|
| _|| _|| _|| _|| _|| _|| _|| _|| _|| _|| _t jd||d| d S )N)is_encoder_decoderforced_eos_token_id )
is_decodertie_word_embeddings
vocab_sizedecoder_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 share_encoder_decoder_embeddingspad_token_ideos_token_idbos_token_iddecoder_start_token_idsuper__init__)selfr   r   r   r   r   r   r!   r    r"   r(   r)   r*   r   r&   r   r#   r$   r%   r'   r1   r,   r.   r/   r0   r   r-   r   r   kwargs	__class__r   m/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/transformers/models/marian/configuration_marian.pyr3   c   sD    

zMarianConfig.__init__)r   Nr   r   r   r   r   r   r   r   r   TTr   r   r   r   r   r   r   Fr   r   Nr   TFT)	__name__
__module____qualname____doc__
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r   N)
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