
    sgz                     Z    d Z ddlmZ ddlmZ  ej
                  e      Z G d de      Zy)zTrOCR model configuration   )PretrainedConfig)loggingc                   b     e Zd ZdZdZdgZddddZ	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 d	 fd	Z xZS )
TrOCRConfiga  
    This is the configuration class to store the configuration of a [`TrOCRForCausalLM`]. It is used to instantiate an
    TrOCR 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 TrOCR
    [microsoft/trocr-base-handwritten](https://huggingface.co/microsoft/trocr-base-handwritten) 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 TrOCR model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`TrOCRForCausalLM`].
        d_model (`int`, *optional*, defaults to 1024):
            Dimensionality of the layers and the pooler layer.
        decoder_layers (`int`, *optional*, defaults to 12):
            Number of decoder layers.
        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.
        activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the pooler. If string, `"gelu"`, `"relu"`,
            `"silu"` and `"gelu_new"` are supported.
        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).
        dropout (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, 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.
        init_std (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        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.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
        scale_embedding (`bool`, *optional*, defaults to `False`):
            Whether or not to scale the word embeddings by sqrt(d_model).
        use_learned_position_embeddings (`bool`, *optional*, defaults to `True`):
            Whether or not to use learned position embeddings. If not, sinusoidal position embeddings will be used.
        layernorm_embedding (`bool`, *optional*, defaults to `True`):
            Whether or not to use a layernorm after the word + position embeddings.

    Example:

    ```python
    >>> from transformers import TrOCRConfig, TrOCRForCausalLM

    >>> # Initializing a TrOCR-base style configuration
    >>> configuration = TrOCRConfig()

    >>> # Initializing a model (with random weights) from the TrOCR-base style configuration
    >>> model = TrOCRForCausalLM(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```trocrpast_key_valuesdecoder_attention_headsd_modeldecoder_layers)num_attention_headshidden_sizenum_hidden_layersc                    || _         || _        || _        || _        || _        || _        || _        || _        |	| _        |
| _	        || _
        || _        || _        || _        || _        || _        t!        | D  d||||d| y )N)pad_token_idbos_token_ideos_token_iddecoder_start_token_id )
vocab_sizer
   r   r	   decoder_ffn_dimactivation_functionmax_position_embeddingsdropoutattention_dropoutactivation_dropoutinit_stddecoder_layerdrop	use_cachescale_embeddinguse_learned_position_embeddingslayernorm_embeddingsuper__init__)selfr   r
   r   r	   r   r   r   r   r   r   r   r   r   r   r   r    r!   r   r   r   kwargs	__class__s                         `/var/www/html/venv/lib/python3.12/site-packages/transformers/models/trocr/configuration_trocr.pyr#   zTrOCRConfig.__init__`   s    0 %,'>$.#6 '>$!2"4 !2"./N,#6  	
%%%#9		

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__module____qualname____doc__
model_typekeys_to_ignore_at_inferenceattribute_mapr#   __classcell__)r&   s   @r'   r   r      sq    =~ J#4"58 -M  "" # (, +/
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r(   r   N)	r3   configuration_utilsr   utilsr   
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