U 9%e)%@sdZddlmZddlmZmZmZmZddlm Z m Z ddl m Z m Z ddlmZmZmZeeZdd d ZGd d d e ZGd dde ZdS)z LayoutLM model configuration OrderedDict)AnyListMappingOptional)PretrainedConfigPreTrainedTokenizer) OnnxConfig PatchingSpec) TensorTypeis_torch_availableloggingzOhttps://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.jsonzPhttps://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json)zmicrosoft/layoutlm-base-uncasedz microsoft/layoutlm-large-uncasedcs&eZdZdZdZdfdd ZZS)LayoutLMConfiga. This is the configuration class to store the configuration of a [`LayoutLMModel`]. It is used to instantiate a LayoutLM 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 LayoutLM [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) architecture. Configuration objects inherit from [`BertConfig`] and can be used to control the model outputs. Read the documentation from [`BertConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 30522): Vocabulary size of the LayoutLM model. Defines the different tokens that can be represented by the *inputs_ids* passed to the forward method of [`LayoutLMModel`]. hidden_size (`int`, *optional*, defaults to 768): Dimensionality 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): Dimensionality 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"`, `"silu"` 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 into [`LayoutLMModel`]. 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-12): The epsilon used by the layer normalization layers. pad_token_id (`int`, *optional*, defaults to 0): The value used to pad input_ids. position_embedding_type (`str`, *optional*, defaults to `"absolute"`): Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155). For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658). use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. classifier_dropout (`float`, *optional*): The dropout ratio for the classification head. max_2d_position_embeddings (`int`, *optional*, defaults to 1024): The maximum value that the 2D position embedding might ever used. Typically set this to something large just in case (e.g., 1024). Examples: ```python >>> from transformers import LayoutLMConfig, LayoutLMModel >>> # Initializing a LayoutLM configuration >>> configuration = LayoutLMConfig() >>> # Initializing a model (with random weights) from the configuration >>> model = LayoutLMModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```Zlayoutlm:w gelu皙?{Gz?-q=rabsoluteTc sttjfd| i|||_||_||_||_||_||_||_||_ | |_ | |_ | |_ | |_ ||_||_||_dS)N pad_token_id)super__init__ vocab_size hidden_sizenum_hidden_layersnum_attention_heads hidden_actintermediate_sizehidden_dropout_probattention_probs_dropout_probmax_position_embeddingstype_vocab_sizeinitializer_rangelayer_norm_epsposition_embedding_type use_cachemax_2d_position_embeddings)selfr r!r"r#r%r$r&r'r(r)r*r+rr,r-r.kwargs __class__r/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/transformers/models/layoutlm/configuration_layoutlm.pyrls zLayoutLMConfig.__init__)rrrrrrrrrrrrrrTr)__name__ __module__ __qualname____doc__Z model_typer __classcell__r3r3r1r4r$s&Erc sxeZdZdeeeedfdd Zee ee e effdddZ de e e e eee eefd fd d ZZS)LayoutLMOnnxConfigdefaultN)configtaskpatching_specscs"tj|||d|jd|_dS)N)r=r>)rrr.Zmax_2d_positions)r/r<r=r>r1r3r4rszLayoutLMOnnxConfig.__init__)returncCs8tddddfddddfddddfddddfgS)N input_idsbatchsequence)rr?bboxZattention_maskZtoken_type_idsr)r/r3r3r4inputss    zLayoutLMOnnxConfig.inputsF) tokenizer batch_size seq_lengthis_pair frameworkr@c s|tj|||||d}ddddg}|tjks4tdtsBtddd l}|d j\}}| |g| |d d |d <|S) a Generate inputs to provide to the ONNX exporter for the specific framework Args: tokenizer: The tokenizer associated with this model configuration batch_size: The batch size (int) to export the model for (-1 means dynamic axis) seq_length: The sequence length (int) to export the model for (-1 means dynamic axis) is_pair: Indicate if the input is a pair (sentence 1, sentence 2) framework: The framework (optional) the tokenizer will generate tensor for Returns: Mapping[str, Tensor] holding the kwargs to provide to the model's forward function )rHrIrJrK0TIzCExporting LayoutLM to ONNX is currently only supported for PyTorch.z7Cannot generate dummy inputs without PyTorch installed.rNrAr?rD) rgenerate_dummy_inputsr ZPYTORCHNotImplementedErrorr ValueErrortorchshapeZtensorZtile) r/rGrHrIrJrKZ input_dictboxrSr1r3r4rPs    z(LayoutLMOnnxConfig.generate_dummy_inputs)r;N)rFrFFN)r5r6r7r strrr rpropertyrintrEr boolrr rrPr9r3r3r1r4r:s*   r:N)r8 collectionsrtypingrrrrr r Zonnxr r utilsr rrZ get_loggerr5loggerZ&LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAPrr:r3r3r3r4s   n