# coding=utf-8
# Copyright 2018, Hao Tan, Mohit Bansal
#
# 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.
""" LXMERT model configuration """


import logging

from .configuration_utils import PretrainedConfig


logger = logging.getLogger(__name__)

LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "unc-nlp/lxmert-base-uncased": "",
}


class LxmertConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a :class:`~transformers.BertModel`.
    It is used to instantiate an Lxmert model according to the specified arguments, defining the model
    architecture.


    Args:
        vocab_size (:obj:`int`, optional, defaults to 30522):
            Vocabulary size of the BERT model. Defines the different tokens that
            can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.BertModel`.
        hidden_size (:obj:`int`, optional, defaults to 768):
            Dimensionality of the encoder layers and the pooler layer.
        r_layers (:obj:`int`, optional, defaults to 5):
            Number of hidden layers in the Transformer visual encoder.
        l_layers (:obj:`int`, optional, defaults to 9):
            Number of hidden layers in the Transformer language encoder.
        x_layers (:obj:`int`, optional, defaults to 5):
            Number of hidden layers in the Transformer cross modality encoder.
        num_attention_heads (:obj:`int`, optional, defaults to 5):
            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 "gelu"):
            The non-linear activation function (function or string) in the encoder and pooler.
            If string, "gelu", "relu", "swish" and "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 `token_type_ids` passed into :class:`~transformers.BertModel`.
        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.
        visual_feat_dim (:obj:`int`, optional, defaults to 2048):
            This represents the last dimension of the pooled-object features used as input for the model,
            representing the size of each object feature itself.
        visual_pos_dim (:obj:`int`, optional, defaults to 4):
            This represents the number of spacial features that are mixed into the visual features.
            The default is set to 4 because most commonly this will represent the location of a bounding box.
            i.e. (x, y, width, height)
        visual_loss_normalizer (:obj:`float`, optional, defaults to 1/15):
            This represents the scaling factor in which each visual loss is multiplied by if during pretraining,
            one decided to train with multiple vision-based loss objectives.
        num_qa_labels (:obj:`int`, optional, defaults to 9500):
            This represents the total number of different question answering (QA) labels there are. If using more than one dataset with QA,
            the user will need to account for the total number of labels that all of the datasets have in total.
        num_object_labels (:obj:`int`, optional, defaults to 1600):
            This represents the total number of semantically unique objects that lxmert will be able to classify a pooled-object feature
            as belonging too.
        num_attr_labels (:obj:`int`, optional, defaults to 400):
            This represents the total number of semantically unique attributes that lxmert will be able to classify a pooled-object feature
            as possessing.
        task_matched (:obj:`bool`, optional, defaults to True):
            This task is used for sentence-image matching. If the sentence correctly describes the image the label will be 1.
            If the sentence does not correctly describe the image, the label will be 0.
        task_mask_lm (:obj:`bool`, optional, defaults to True):
            This task is the defacto masked langauge modeling used in pretraining models such as BERT.
        task_obj_predict (:obj:`bool`, optional, defaults to True):
            This task is set to true if the user would like to perform one of the following loss objectives:
            object predicition, atrribute predicition, feature regression
        task_qa (:obj:`bool`, optional, defaults to True):
            This task specifies whether or not Lxmert will calculate the question-asnwering loss objective
        visual_obj_loss (:obj:`bool`, optional, defaults to True):
            This task specifies whether or not Lxmert will calculate the object-prediction loss objective
        visual_attr_loss (:obj:`bool`, optional, defaults to True):
            This task specifies whether or not Lxmert will calculate the attribute-prediction loss objective
        visual_feat_loss (:obj:`bool`, optional, defaults to True):
            This task specifies whether or not Lxmert will calculate the feature-regression loss objective
        output_attentions (:obj:`bool`, optional, defaults to False):
                if True, the vision, langauge, and cross-modality layers will be returned
        output_hidden_states (:obj:`bool`, optional, defaults to False):
                if True, final cross-modality hidden states for language and vision features will be returned

    """

    model_type = "lxmert"

    def __init__(
        self,
        vocab_size=30522,
        hidden_size=768,
        num_attention_heads=12,
        num_labels=2,
        num_qa_labels=9500,
        num_object_labels=1600,
        num_attr_labels=400,
        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,
        l_layers=9,
        x_layers=5,
        r_layers=5,
        visual_feat_dim=2048,
        visual_pos_dim=4,
        visual_loss_normalizer=6.67,
        task_matched=True,
        task_mask_lm=True,
        task_obj_predict=True,
        task_qa=True,
        visual_obj_loss=True,
        visual_attr_loss=True,
        visual_feat_loss=True,
        output_attentions=False,
        output_hidden_states=False,
        **kwargs,
    ):
        super().__init__(**kwargs)
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_attention_heads = num_attention_heads
        self.num_labels = num_labels
        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.num_qa_labels = num_qa_labels
        self.num_object_labels = num_object_labels
        self.num_attr_labels = num_attr_labels
        self.l_layers = l_layers
        self.x_layers = x_layers
        self.r_layers = r_layers
        self.visual_feat_dim = visual_feat_dim
        self.visual_pos_dim = visual_pos_dim
        self.visual_loss_normalizer = visual_loss_normalizer
        self.task_matched = task_matched
        self.task_mask_lm = task_mask_lm
        self.task_obj_predict = task_obj_predict
        self.task_qa = task_qa
        self.visual_obj_loss = visual_obj_loss
        self.visual_attr_loss = visual_attr_loss
        self.visual_feat_loss = visual_feat_loss
        self.output_hidden_states = output_hidden_states
        self.output_attentions = self.output_attentions
        self.num_hidden_layers = {"vision": r_layers, "cross_encoder": x_layers, "language": l_layers}
