# coding=utf-8
# Copyright 2010, The Microsoft Research Asia LayoutLM Team authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" LayoutLM model configuration """


from .configuration_bert import BertConfig
from .utils import logging


logger = logging.get_logger(__name__)

LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "layoutlm-base-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/microsoft/layoutlm-base-uncased/config.json",
    "layoutlm-large-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/microsoft/layoutlm-large-uncased/config.json",
}


class LayoutLMConfig(BertConfig):
    r"""
    This is the configuration class to store the configuration of a :class:`~transformers.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 `layoutlm-base-uncased
    <https://huggingface.co/microsoft/layoutlm-base-uncased>`__ architecture.

    Configuration objects inherit from :class:`~transformers.BertConfig` and can be used to control the model outputs.
    Read the documentation from :class:`~transformers.BertConfig` for more information.


    Args:
        vocab_size (:obj:`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 :class:`~transformers.LayoutLMModel`.
        hidden_size (:obj:`int`, `optional`, defaults to 768):
            Dimensionality of the encoder layers and the pooler layer.
        num_hidden_layers (:obj:`int`, `optional`, defaults to 12):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (:obj:`int`, `optional`, defaults to 12):
            Number of attention heads for each attention layer in the Transformer encoder.
        intermediate_size (:obj:`int`, `optional`, defaults to 3072):
            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
        hidden_act (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string,
            :obj:`"gelu"`, :obj:`"relu"`, :obj:`"swish"` and :obj:`"gelu_new"` are supported.
        hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
            The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
        attention_probs_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
            The dropout ratio for the attention probabilities.
        max_position_embeddings (:obj:`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 (:obj:`int`, `optional`, defaults to 2):
            The vocabulary size of the :obj:`token_type_ids` passed into :class:`~transformers.LayoutLMModel`.
        initializer_range (:obj:`float`, `optional`, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        layer_norm_eps (:obj:`float`, `optional`, defaults to 1e-12):
            The epsilon used by the layer normalization layers.
        gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`):
            If True, use gradient checkpointing to save memory at the expense of slower backward pass.
        max_2d_position_embeddings (:obj:`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::

        >>> from transformers import LayoutLMModel, LayoutLMConfig

        >>> # Initializing a LayoutLM configuration
        >>> configuration = LayoutLMConfig()

        >>> # Initializing a model 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,
        gradient_checkpointing=False,
        max_2d_position_embeddings=1024,
        **kwargs
    ):
        super().__init__(
            vocab_size=vocab_size,
            hidden_size=hidden_size,
            num_hidden_layers=num_hidden_layers,
            num_attention_heads=num_attention_heads,
            intermediate_size=intermediate_size,
            hidden_act=hidden_act,
            hidden_dropout_prob=hidden_dropout_prob,
            attention_probs_dropout_prob=attention_probs_dropout_prob,
            max_position_embeddings=max_position_embeddings,
            type_vocab_size=type_vocab_size,
            initializer_range=initializer_range,
            layer_norm_eps=layer_norm_eps,
            pad_token_id=pad_token_id,
            gradient_checkpointing=gradient_checkpointing,
            **kwargs,
        )
        self.max_2d_position_embeddings = max_2d_position_embeddings
