T^(h3dZddlmZddlmZmZmZmZddlm Z ddl m Z ddl m Z ddlmZdd lmZer dd lmZdd lmZej*eZGd d e ZGdde Zd dgZy)zLayoutLMv3 model configuration) OrderedDict) TYPE_CHECKINGAnyMappingOptional)version)PretrainedConfig) OnnxConfig) compute_effective_axis_dimension)logging)ProcessorMixin) TensorTypecdeZdZdZdZ dfd ZxZS)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_sizenum_hidden_layersnum_attention_headsintermediate_size hidden_acthidden_dropout_probattention_probs_dropout_probmax_position_embeddingstype_vocab_sizeinitializer_rangelayer_norm_eps pad_token_id bos_token_id eos_token_id)super__init__max_2d_position_embeddingscoordinate_size shape_sizehas_relative_attention_bias rel_pos_bins max_rel_poshas_spatial_attention_biasrel_2d_pos_binsmax_rel_2d_pos text_embed visual_embed input_size num_channels patch_sizeclassifier_dropout)!selfrrrrrrrrrrrrr r!r"r&r'r(r)r*r+r-r.r,r/r0r1r2r3r4kwargs __class__s! u/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/transformers/models/layoutlmv3/configuration_layoutlmv3.pyr%zLayoutLMv3Config.__init__ysD  !#/ 3/! 3)E$;+/)%%% ! $+E'.$+F((&*D'.,$($($"4)iYi r:i gelu皙?r<ig{Gz?h㈵>rr=ir@T r@@TTTr N)__name__ __module__ __qualname____doc__ model_typer% __classcell__)r7s@r8rr$syP dJ%( ##'$(#'?B5B5r9rceZdZejdZedeeee efffdZ ede fdZ ede fdZ dddd e d e d ed ed de de de deeeffdZy)LayoutLMv3OnnxConfigz1.12returnc |jdvr%tddddfddddfddddfddd d d d fgStddddfddddfddddfddd dfgS) N)zquestion-answeringzsequence-classification input_idsbatchsequence)rr?attention_maskbbox pixel_valuesr2heightwidth)rr?r=r )taskrr5s r8inputszLayoutLMv3OnnxConfig.inputss 99I I g*"=>%7z'BCZ89#^U\%]^   g*"=>Z89%7z'BC#^%DE  r9cy)Nr>r#rYs r8atol_for_validationz(LayoutLMv3OnnxConfig.atol_for_validationsr9cy)Nr:r#rYs r8default_onnx_opsetz'LayoutLMv3OnnxConfig.default_onnx_opsetsr9N processorr batch_size seq_lengthis_pair frameworkrr2 image_width image_heightc t|jddt|tjd}|j j |} t|tj| }dj|j jg|zgg|z} gdgg|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_dimensionnum_token_to_add )0TIr@)textboxesreturn_tensors) setattrimage_processorr r default_fixed_batch tokenizernum_special_tokens_to_adddefault_fixed_sequencejoin unk_token_generate_dummy_imagesdict)r5r_r`rarbrcr2rdre token_to_add dummy_text dummy_bboxes dummy_imagerZs r8generate_dummy_inputsz*LayoutLMv3OnnxConfig.generate_dummy_inputssJ  ));>6  (F(FYZ !**DDWM 5  (I(I\h xx!4!4!>!> ?@:MNOR\\ ++,z9 11*lLZef  "(    r9)rFNr (r)rFrGrHrparsetorch_onnx_minimum_versionpropertyrstrintrZfloatr\r^boolrrrr#r9r8rMrMs!.v!6 WS#X%6 67*UC ,0C#CC C  C L) CCCC c Cr9rMN)rI collectionsrtypingrrrr packagingrconfiguration_utilsr onnxr onnx.utilsr utilsr processing_utilsrr get_loggerrFloggerrrM__all__r#r9r8rsm%#883:2#   H %W5'W5td:dN 5 6r9