# coding=utf-8
# Copyright 2021 Facebook AI Research and The HuggingFace Inc. team. All rights reserved.
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#     http://www.apache.org/licenses/LICENSE-2.0
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""" DETR model configuration """

from ...configuration_utils import PretrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)

DETR_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "facebook/detr-resnet-50": "https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json",
    # See all DETR models at https://huggingface.co/models?filter=detr
}


class DetrConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a :class:`~transformers.DetrModel`. It is used to
    instantiate a DETR 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 DETR `facebook/detr-resnet-50
    <https://huggingface.co/facebook/detr-resnet-50>`__ architecture.

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


    Args:
        num_queries (:obj:`int`, `optional`, defaults to 100):
            Number of object queries, i.e. detection slots. This is the maximal number of objects
            :class:`~transformers.DetrModel` can detect in a single image. For COCO, we recommend 100 queries.
        d_model (:obj:`int`, `optional`, defaults to 256):
            Dimension of the layers.
        encoder_layers (:obj:`int`, `optional`, defaults to 6):
            Number of encoder layers.
        decoder_layers (:obj:`int`, `optional`, defaults to 6):
            Number of decoder layers.
        encoder_attention_heads (:obj:`int`, `optional`, defaults to 8):
            Number of attention heads for each attention layer in the Transformer encoder.
        decoder_attention_heads (:obj:`int`, `optional`, defaults to 8):
            Number of attention heads for each attention layer in the Transformer decoder.
        decoder_ffn_dim (:obj:`int`, `optional`, defaults to 2048):
            Dimension of the "intermediate" (often named feed-forward) layer in decoder.
        encoder_ffn_dim (:obj:`int`, `optional`, defaults to 2048):
            Dimension of the "intermediate" (often named feed-forward) layer in decoder.
        activation_function (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"relu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string,
            :obj:`"gelu"`, :obj:`"relu"`, :obj:`"silu"` and :obj:`"gelu_new"` are supported.
        dropout (:obj:`float`, `optional`, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        attention_dropout (:obj:`float`, `optional`, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        activation_dropout (:obj:`float`, `optional`, defaults to 0.0):
            The dropout ratio for activations inside the fully connected layer.
        classifier_dropout (:obj:`float`, `optional`, defaults to 0.0):
            The dropout ratio for classifier.
        max_position_embeddings (:obj:`int`, `optional`, defaults to 1024):
            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).
        init_std (:obj:`float`, `optional`, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        init_xavier_std (:obj:`float`, `optional`, defaults to 1):
            The scaling factor used for the Xavier initialization gain in the HM Attention map module.
        encoder_layerdrop: (:obj:`float`, `optional`, defaults to 0.0):
            The LayerDrop probability for the encoder. See the `LayerDrop paper <see
            https://arxiv.org/abs/1909.11556>`__ for more details.
        decoder_layerdrop: (:obj:`float`, `optional`, defaults to 0.0):
            The LayerDrop probability for the decoder. See the `LayerDrop paper <see
            https://arxiv.org/abs/1909.11556>`__ for more details.
        auxiliary_loss (:obj:`bool`, `optional`, defaults to :obj:`False`):
            Whether auxiliary decoding losses (loss at each decoder layer) are to be used.
        position_embedding_type (:obj:`str`, `optional`, defaults to :obj:`"sine"`):
            Type of position embeddings to be used on top of the image features. One of :obj:`"sine"` or
            :obj:`"learned"`.
        backbone (:obj:`str`, `optional`, defaults to :obj:`"resnet50"`):
            Name of convolutional backbone to use. Supports any convolutional backbone from the timm package. For a
            list of all available models, see `this page
            <https://rwightman.github.io/pytorch-image-models/#load-a-pretrained-model>`__.
        dilation (:obj:`bool`, `optional`, defaults to :obj:`False`):
            Whether to replace stride with dilation in the last convolutional block (DC5).
        class_cost (:obj:`float`, `optional`, defaults to 1):
            Relative weight of the classification error in the Hungarian matching cost.
        bbox_cost (:obj:`float`, `optional`, defaults to 5):
            Relative weight of the L1 error of the bounding box coordinates in the Hungarian matching cost.
        giou_cost (:obj:`float`, `optional`, defaults to 2):
            Relative weight of the generalized IoU loss of the bounding box in the Hungarian matching cost.
        mask_loss_coefficient (:obj:`float`, `optional`, defaults to 1):
            Relative weight of the Focal loss in the panoptic segmentation loss.
        dice_loss_coefficient (:obj:`float`, `optional`, defaults to 1):
            Relative weight of the DICE/F-1 loss in the panoptic segmentation loss.
        bbox_loss_coefficient (:obj:`float`, `optional`, defaults to 5):
            Relative weight of the L1 bounding box loss in the object detection loss.
        giou_loss_coefficient (:obj:`float`, `optional`, defaults to 2):
            Relative weight of the generalized IoU loss in the object detection loss.
        eos_coefficient (:obj:`float`, `optional`, defaults to 0.1):
            Relative classification weight of the 'no-object' class in the object detection loss.

    Examples::

        >>> from transformers import DetrModel, DetrConfig

        >>> # Initializing a DETR facebook/detr-resnet-50 style configuration
        >>> configuration = DetrConfig()

        >>> # Initializing a model from the facebook/detr-resnet-50 style configuration
        >>> model = DetrModel(configuration)

        >>> # Accessing the model configuration
        >>> configuration = model.config
    """
    model_type = "detr"
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(
        self,
        num_queries=100,
        max_position_embeddings=1024,
        encoder_layers=6,
        encoder_ffn_dim=2048,
        encoder_attention_heads=8,
        decoder_layers=6,
        decoder_ffn_dim=2048,
        decoder_attention_heads=8,
        encoder_layerdrop=0.0,
        decoder_layerdrop=0.0,
        is_encoder_decoder=True,
        activation_function="relu",
        d_model=256,
        dropout=0.1,
        attention_dropout=0.0,
        activation_dropout=0.0,
        init_std=0.02,
        init_xavier_std=1.0,
        classifier_dropout=0.0,
        scale_embedding=False,
        auxiliary_loss=False,
        position_embedding_type="sine",
        backbone="resnet50",
        dilation=False,
        class_cost=1,
        bbox_cost=5,
        giou_cost=2,
        mask_loss_coefficient=1,
        dice_loss_coefficient=1,
        bbox_loss_coefficient=5,
        giou_loss_coefficient=2,
        eos_coefficient=0.1,
        **kwargs
    ):
        super().__init__(is_encoder_decoder=is_encoder_decoder, **kwargs)

        self.num_queries = num_queries
        self.max_position_embeddings = max_position_embeddings
        self.d_model = d_model
        self.encoder_ffn_dim = encoder_ffn_dim
        self.encoder_layers = encoder_layers
        self.encoder_attention_heads = encoder_attention_heads
        self.decoder_ffn_dim = decoder_ffn_dim
        self.decoder_layers = decoder_layers
        self.decoder_attention_heads = decoder_attention_heads
        self.dropout = dropout
        self.attention_dropout = attention_dropout
        self.activation_dropout = activation_dropout
        self.activation_function = activation_function
        self.init_std = init_std
        self.init_xavier_std = init_xavier_std
        self.encoder_layerdrop = encoder_layerdrop
        self.decoder_layerdrop = decoder_layerdrop
        self.classifier_dropout = classifier_dropout
        self.num_hidden_layers = encoder_layers
        self.scale_embedding = scale_embedding  # scale factor will be sqrt(d_model) if True
        self.auxiliary_loss = auxiliary_loss
        self.position_embedding_type = position_embedding_type
        self.backbone = backbone
        self.dilation = dilation
        # Hungarian matcher
        self.class_cost = class_cost
        self.bbox_cost = bbox_cost
        self.giou_cost = giou_cost
        # Loss coefficients
        self.mask_loss_coefficient = mask_loss_coefficient
        self.dice_loss_coefficient = dice_loss_coefficient
        self.bbox_loss_coefficient = bbox_loss_coefficient
        self.giou_loss_coefficient = giou_loss_coefficient
        self.eos_coefficient = eos_coefficient

    @property
    def num_attention_heads(self) -> int:
        return self.encoder_attention_heads

    @property
    def hidden_size(self) -> int:
        return self.d_model
