AutoAndroidController/py/venv/Lib/site-packages/transformers/models/layoutlm/configuration_layoutlm.py

# coding=utf-8
# Copyright 2010, The Microsoft Research Asia LayoutLM Team authors
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""LayoutLM model configuration"""

from collections import OrderedDict
from collections.abc import Mapping
from typing import Any, Optional

from ... import PretrainedConfig, PreTrainedTokenizer
from ...onnx import OnnxConfig, PatchingSpec
from ...utils import TensorType, is_torch_available, logging


logger = logging.get_logger(__name__)


class LayoutLMConfig(PretrainedConfig):
    r"""
    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.
        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`.
        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
    ```"""

    model_type = "layoutlm"

    def __init__(
        self,
        vocab_size=30522,
        hidden_size=768,
        num_hidden_layers=12,
        num_attention_heads=12,
        intermediate_size=3072,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=512,
        type_vocab_size=2,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        pad_token_id=0,
        use_cache=True,
        max_2d_position_embeddings=1024,
        **kwargs,
    ):
        super().__init__(pad_token_id=pad_token_id, **kwargs)
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.hidden_act = hidden_act
        self.intermediate_size = intermediate_size
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.max_position_embeddings = max_position_embeddings
        self.type_vocab_size = type_vocab_size
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.use_cache = use_cache
        self.max_2d_position_embeddings = max_2d_position_embeddings


class LayoutLMOnnxConfig(OnnxConfig):
    def __init__(
        self,
        config: PretrainedConfig,
        task: str = "default",
        patching_specs: Optional[list[PatchingSpec]] = None,
    ):
        super().__init__(config, task=task, patching_specs=patching_specs)
        self.max_2d_positions = config.max_2d_position_embeddings - 1

    @property
    def inputs(self) -> Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ("input_ids", {0: "batch", 1: "sequence"}),
                ("bbox", {0: "batch", 1: "sequence"}),
                ("attention_mask", {0: "batch", 1: "sequence"}),
                ("token_type_ids", {0: "batch", 1: "sequence"}),
            ]
        )

    def generate_dummy_inputs(
        self,
        tokenizer: PreTrainedTokenizer,
        batch_size: int = -1,
        seq_length: int = -1,
        is_pair: bool = False,
        framework: Optional[TensorType] = None,
    ) -> Mapping[str, Any]:
        """
        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
        """

        input_dict = super().generate_dummy_inputs(
            tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
        )

        # Generate a dummy bbox
        box = [48, 84, 73, 128]

        if not framework == TensorType.PYTORCH:
            raise NotImplementedError("Exporting LayoutLM to ONNX is currently only supported for PyTorch.")

        if not is_torch_available():
            raise ValueError("Cannot generate dummy inputs without PyTorch installed.")
        import torch

        batch_size, seq_length = input_dict["input_ids"].shape
        input_dict["bbox"] = torch.tensor([*[box] * seq_length]).tile(batch_size, 1, 1)
        return input_dict


__all__ = ["LayoutLMConfig", "LayoutLMOnnxConfig"]