
    sg3                         d Z ddlmZ ddlmZmZmZmZ ddlm	Z	 ddl
mZ ddlmZ ddlmZ dd	lmZ erdd
lmZ ddlmZ  ej*                  e      Z G d de      Z G d de      Zy)zLayoutLMv3 model configuration    )OrderedDict)TYPE_CHECKINGAnyMappingOptional)version   )PretrainedConfig)
OnnxConfig) compute_effective_axis_dimension)logging)ProcessorMixin)
TensorTypec                   d     e Zd ZdZdZ	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 d fd	Z xZS )LayoutLMv3Configa  
    This is the configuration class to store the configuration of a [`LayoutLMv3Model`]. It is used to instantiate an
    LayoutLMv3 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 LayoutLMv3
    [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) 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 LayoutLMv3 model. Defines the number of different tokens that can be represented by
            the `inputs_ids` passed when calling [`LayoutLMv3Model`].
        hidden_size (`int`, *optional*, defaults to 768):
            Dimension of the encoder layers and the pooler layer.
        num_hidden_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer encoder.
        intermediate_size (`int`, *optional*, defaults to 3072):
            Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"selu"` and `"gelu_new"` are supported.
        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout ratio for the attention probabilities.
        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).
        type_vocab_size (`int`, *optional*, defaults to 2):
            The vocabulary size of the `token_type_ids` passed when calling [`LayoutLMv3Model`].
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        layer_norm_eps (`float`, *optional*, defaults to 1e-5):
            The epsilon used by the layer normalization layers.
        max_2d_position_embeddings (`int`, *optional*, defaults to 1024):
            The maximum value that the 2D position embedding might ever be used with. Typically set this to something
            large just in case (e.g., 1024).
        coordinate_size (`int`, *optional*, defaults to `128`):
            Dimension of the coordinate embeddings.
        shape_size (`int`, *optional*, defaults to `128`):
            Dimension of the width and height embeddings.
        has_relative_attention_bias (`bool`, *optional*, defaults to `True`):
            Whether or not to use a relative attention bias in the self-attention mechanism.
        rel_pos_bins (`int`, *optional*, defaults to 32):
            The number of relative position bins to be used in the self-attention mechanism.
        max_rel_pos (`int`, *optional*, defaults to 128):
            The maximum number of relative positions to be used in the self-attention mechanism.
        max_rel_2d_pos (`int`, *optional*, defaults to 256):
            The maximum number of relative 2D positions in the self-attention mechanism.
        rel_2d_pos_bins (`int`, *optional*, defaults to 64):
            The number of 2D relative position bins in the self-attention mechanism.
        has_spatial_attention_bias (`bool`, *optional*, defaults to `True`):
            Whether or not to use a spatial attention bias in the self-attention mechanism.
        visual_embed (`bool`, *optional*, defaults to `True`):
            Whether or not to add patch embeddings.
        input_size (`int`, *optional*, defaults to `224`):
            The size (resolution) of the images.
        num_channels (`int`, *optional*, defaults to `3`):
            The number of channels of the images.
        patch_size (`int`, *optional*, defaults to `16`)
            The size (resolution) of the patches.
        classifier_dropout (`float`, *optional*):
            The dropout ratio for the classification head.

    Example:

    ```python
    >>> from transformers import LayoutLMv3Config, LayoutLMv3Model

    >>> # Initializing a LayoutLMv3 microsoft/layoutlmv3-base style configuration
    >>> configuration = LayoutLMv3Config()

    >>> # Initializing a model (with random weights) from the microsoft/layoutlmv3-base style configuration
    >>> model = LayoutLMv3Model(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```
layoutlmv3c                    t         |   d|||||||||	|
|||||d| || _        || _        || _        || _        || _        || _        || _        || _	        || _
        || _        || _        || _        || _        || _        || _        y )N)
vocab_sizehidden_sizenum_hidden_layersnum_attention_headsintermediate_size
hidden_acthidden_dropout_probattention_probs_dropout_probmax_position_embeddingstype_vocab_sizeinitializer_rangelayer_norm_epspad_token_idbos_token_ideos_token_id )super__init__max_2d_position_embeddingscoordinate_size
shape_sizehas_relative_attention_biasrel_pos_binsmax_rel_poshas_spatial_attention_biasrel_2d_pos_binsmax_rel_2d_pos
text_embedvisual_embed
input_sizenum_channels
patch_sizeclassifier_dropout)!selfr   r   r   r   r   r   r   r   r   r   r   r   r    r!   r"   r&   r'   r(   r)   r*   r+   r-   r.   r,   r/   r0   r1   r2   r3   r4   kwargs	__class__s!                                   j/var/www/html/venv/lib/python3.12/site-packages/transformers/models/layoutlmv3/configuration_layoutlmv3.pyr%   zLayoutLMv3Config.__init__y   s    D 	 	
!#/ 3/! 3)E$;+/)%%%	
  !	
$ +E'.$+F((&*D'.,$($($"4    )iY  i      r:   i   gelu皙?r<   i      g{Gz?h㈵>   r   r=   i      r@   T    r@   @      TTT   r	      N)__name__
__module____qualname____doc__
model_typer%   __classcell__)r7   s   @r8   r   r   $   sy    Pd J %( ##'$(#'?B5 B5r9   r   c                       e Zd Z ej                  d      Zedeeee	ef   f   fd       Z
edefd       Zede	fd       Z	 	 	 	 	 	 	 dddd	e	d
e	deded   de	de	de	deeef   fdZy)LayoutLMv3OnnxConfigz1.12returnc                     | j                   dv r%t        ddddfddddfddddfddd	d
ddfg      S t        ddddfddddfddddfddd	dfg      S )N)zquestion-answeringzsequence-classification	input_idsbatchsequence)r   r?   attention_maskbboxpixel_valuesr2   heightwidth)r   r?   r=   r	   )taskr   r5   s    r8   inputszLayoutLMv3OnnxConfig.inputs   s     99II g*"=>%7z'BCZ89#^U\%]^	   g*"=>Z89%7z'BC#^%DE	 r9   c                      y)Nr>   r#   rY   s    r8   atol_for_validationz(LayoutLMv3OnnxConfig.atol_for_validation   s    r9   c                      y)Nr:   r#   rY   s    r8   default_onnx_opsetz'LayoutLMv3OnnxConfig.default_onnx_opset   s    r9   N	processorr   
batch_size
seq_lengthis_pair	frameworkr   r2   image_widthimage_heightc	                    t        |j                  dd       t        |t        j                  d      }|j
                  j                  |      }	t        |t        j                  |	      }dj                  |j
                  j                  g      |z  gg|z  }
g dgg|z  }| j                  ||||      }t         |||
||            }|S )a  
        Generate inputs to provide to the ONNX exporter for the specific framework

        Args:
            processor ([`ProcessorMixin`]):
                The processor associated with this model configuration.
            batch_size (`int`, *optional*, defaults to -1):
                The batch size to export the model for (-1 means dynamic axis).
            seq_length (`int`, *optional*, defaults to -1):
                The sequence length to export the model for (-1 means dynamic axis).
            is_pair (`bool`, *optional*, defaults to `False`):
                Indicate if the input is a pair (sentence 1, sentence 2).
            framework (`TensorType`, *optional*, defaults to `None`):
                The framework (PyTorch or TensorFlow) that the processor will generate tensors for.
            num_channels (`int`, *optional*, defaults to 3):
                The number of channels of the generated images.
            image_width (`int`, *optional*, defaults to 40):
                The width of the generated images.
            image_height (`int`, *optional*, defaults to 40):
                The height of the generated images.

        Returns:
            Mapping[str, Any]: holding the kwargs to provide to the model's forward function
        	apply_ocrFr   )fixed_dimensionnum_token_to_add )0   T   I   r@   )textboxesreturn_tensors)setattrimage_processorr   r   default_fixed_batch	tokenizernum_special_tokens_to_adddefault_fixed_sequencejoin	unk_token_generate_dummy_imagesdict)r5   r_   r`   ra   rb   rc   r2   rd   re   token_to_add
dummy_textdummy_bboxesdummy_imagerZ   s                 r8   generate_dummy_inputsz*LayoutLMv3OnnxConfig.generate_dummy_inputs   s    J 		));> 6
(F(FYZ

 !**DDWM5
(I(I\h

 xx!4!4!>!> ?@:MNOR\\
 ++,z9 11*lLZef"(	
 r9   )r   FNr	   (   r   )rF   rG   rH   r   parsetorch_onnx_minimum_versionpropertyr   strintrZ   floatr\   r^   boolr   r   r   r#   r9   r8   rM   rM      s   !.v!6WS#X%6 67  * U   C   ,0C#C C 	C
 C L)C C C C 
c	Cr9   rM   N)rI   collectionsr   typingr   r   r   r   	packagingr   configuration_utilsr
   onnxr   
onnx.utilsr   utilsr   processing_utilsr   r   
get_loggerrF   loggerr   rM   r#   r9   r8   <module>r      s_    % # 8 8  3  :  2# 
		H	%W5' W5td: dr9   