# Copyright 2025 The HuggingFace Inc. team.
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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
#
# Unless required by applicable law or agreed to in writing, software
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import math

import torch
from huggingface_hub.dataclasses import strict
from torch import nn

from ... import initialization as init
from ...backbone_utils import consolidate_backbone_kwargs_to_config
from ...configuration_utils import PreTrainedConfig
from ...utils import auto_docstring, logging
from ..auto import CONFIG_MAPPING, AutoConfig
from ..auto.modeling_auto import AutoModel
from ..grounding_dino.modeling_grounding_dino import (
    GroundingDinoContrastiveEmbedding,
    GroundingDinoConvEncoder,
    GroundingDinoConvModel,
    GroundingDinoDecoder,
    GroundingDinoEncoder,
    GroundingDinoForObjectDetection,
    GroundingDinoMLPPredictionHead,
    GroundingDinoModel,
    GroundingDinoPreTrainedModel,
    build_position_encoding,
)


logger = logging.get_logger(__name__)


@auto_docstring(checkpoint="openmmlab-community/mm_grounding_dino_tiny_o365v1_goldg_v3det")
@strict
class MMGroundingDinoConfig(PreTrainedConfig):
    r"""
    num_queries (`int`, *optional*, defaults to 900):
        Number of object queries, i.e. detection slots. This is the maximal number of objects
        [`MMGroundingDinoModel`] can detect in a single image.
    auxiliary_loss (`bool`, *optional*, defaults to `False`):
        Whether auxiliary decoding losses (loss at each decoder layer) are to be used.
    position_embedding_type (`str`, *optional*, defaults to `"sine"`):
        Type of position embeddings to be used on top of the image features. One of `"sine"` or `"learned"`.
    num_feature_levels (`int`, *optional*, defaults to 4):
        The number of input feature levels.
    encoder_n_points (`int`, *optional*, defaults to 4):
        The number of sampled keys in each feature level for each attention head in the encoder.
    decoder_n_points (`int`, *optional*, defaults to 4):
        The number of sampled keys in each feature level for each attention head in the decoder.
    two_stage (`bool`, *optional*, defaults to `True`):
        Whether to apply a two-stage deformable DETR, where the region proposals are also generated by a variant of
        Grounding DINO, which are further fed into the decoder for iterative bounding box refinement.
    disable_custom_kernels (`bool`, *optional*, defaults to `False`):
        Disable the use of custom CUDA and CPU kernels. This option is necessary for the ONNX export, as custom
        kernels are not supported by PyTorch ONNX export.
    max_text_len (`int`, *optional*, defaults to 256):
        The maximum length of the text input.
    text_enhancer_dropout (`float`, *optional*, defaults to 0.0):
        The dropout ratio for the text enhancer.
    fusion_droppath (`float`, *optional*, defaults to 0.1):
        The droppath ratio for the fusion module.
    fusion_dropout (`float`, *optional*, defaults to 0.0):
        The dropout ratio for the fusion module.
    embedding_init_target (`bool`, *optional*, defaults to `True`):
        Whether to initialize the target with Embedding weights.
    query_dim (`int`, *optional*, defaults to 4):
        The dimension of the query vector.
    positional_embedding_temperature (`float`, *optional*, defaults to 20):
        The temperature for Sine Positional Embedding that is used together with vision backbone.

    Examples:

    ```python
    >>> from transformers import MMGroundingDinoConfig, MMGroundingDinoModel

    >>> # Initializing a MM Grounding DINO configuration
    >>> configuration = MMGroundingDinoConfig()

    >>> # Initializing a model (with random weights) from the configuration
    >>> model = MMGroundingDinoModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = "mm-grounding-dino"
    sub_configs = {"backbone_config": AutoConfig, "text_config": AutoConfig}
    attribute_map = {
        "hidden_size": "d_model",
        "num_attention_heads": "encoder_attention_heads",
    }

    backbone_config: dict | PreTrainedConfig | None = None
    text_config: dict | PreTrainedConfig | None = None
    num_queries: int = 900
    encoder_layers: int = 6
    encoder_ffn_dim: int = 2048
    encoder_attention_heads: int = 8
    decoder_layers: int = 6
    decoder_ffn_dim: int = 2048
    decoder_attention_heads: int = 8
    is_encoder_decoder: bool = True
    activation_function: str = "relu"
    d_model: int = 256
    dropout: float | int = 0.1
    attention_dropout: float | int = 0.0
    activation_dropout: float | int = 0.0
    auxiliary_loss: bool = False
    position_embedding_type: str = "sine"
    num_feature_levels: int = 4
    encoder_n_points: int = 4
    decoder_n_points: int = 4
    two_stage: bool = True
    class_cost: float = 1.0
    bbox_cost: float = 5.0
    giou_cost: float = 2.0
    bbox_loss_coefficient: float = 5.0
    giou_loss_coefficient: float = 2.0
    focal_alpha: float = 0.25
    disable_custom_kernels: bool = False
    max_text_len: int = 256
    text_enhancer_dropout: float | int = 0.0
    fusion_droppath: float | int = 0.1
    fusion_dropout: float | int = 0.0
    embedding_init_target: bool = True
    query_dim: int = 4
    positional_embedding_temperature: int = 20
    init_std: float = 0.02
    layer_norm_eps: float = 1e-5
    tie_word_embeddings: bool = True

    def __post_init__(self, **kwargs):
        self.backbone_config, kwargs = consolidate_backbone_kwargs_to_config(
            backbone_config=self.backbone_config,
            default_config_type="swin",
            default_config_kwargs={"out_indices": [2, 3, 4]},
            **kwargs,
        )

        # Text backbone
        if isinstance(self.text_config, dict):
            self.text_config["model_type"] = self.text_config.get("model_type", "bert")
            self.text_config = CONFIG_MAPPING[self.text_config["model_type"]](**self.text_config)
        elif self.text_config is None:
            logger.info("text_config is None. Initializing the text config with default values (`BertConfig`).")
            self.text_config = CONFIG_MAPPING["bert"]()

        super().__post_init__(**kwargs)


class MMGroundingDinoContrastiveEmbedding(GroundingDinoContrastiveEmbedding):
    def __init__(self, config):
        super().__init__(config)
        self.bias = nn.Parameter(torch.tensor(0.0))

    def forward(
        self,
        vision_hidden_state: torch.FloatTensor,
        text_hidden_state: torch.FloatTensor,
        text_token_mask: torch.BoolTensor,
    ) -> torch.FloatTensor:
        res = vision_hidden_state @ text_hidden_state.transpose(-1, -2)
        res = res / math.sqrt(vision_hidden_state.shape[-1])
        res = res + self.bias
        res.masked_fill_(~text_token_mask[:, None, :], float("-inf"))

        # padding to max_text_len
        new_res = torch.full((*res.shape[:-1], self.max_text_len), float("-inf"), device=res.device)
        new_res[..., : res.shape[-1]] = res

        return new_res


class MMGroundingDinoPreTrainedModel(GroundingDinoPreTrainedModel):
    @torch.no_grad()
    def _init_weights(self, module):
        super()._init_weights(module)
        if isinstance(module, MMGroundingDinoContrastiveEmbedding):
            init.constant_(module.bias, -math.log((1 - 0.01) / 0.01))


class MMGroundingDinoConvEncoder(GroundingDinoConvEncoder):
    pass


class MMGroundingDinoConvModel(GroundingDinoConvModel):
    pass


class MMGroundingDinoEncoder(GroundingDinoEncoder):
    pass


class MMGroundingDinoDecoder(GroundingDinoDecoder):
    pass


class MMGroundingDinoModel(GroundingDinoModel, MMGroundingDinoPreTrainedModel):
    def __init__(self, config: MMGroundingDinoConfig):
        MMGroundingDinoPreTrainedModel.__init__(self, config)

        # Create backbone + positional encoding
        backbone = MMGroundingDinoConvEncoder(config)
        position_embeddings = build_position_encoding(config)
        self.backbone = MMGroundingDinoConvModel(backbone, position_embeddings)

        # Create input projection layers
        num_backbone_outs = len(backbone.intermediate_channel_sizes)
        input_proj_list = []
        for i in range(num_backbone_outs):
            in_channels = backbone.intermediate_channel_sizes[i]
            input_proj_list.append(
                nn.Sequential(
                    nn.Conv2d(in_channels, config.d_model, kernel_size=1),
                    nn.GroupNorm(32, config.d_model),
                )
            )
        for _ in range(config.num_feature_levels - num_backbone_outs):
            input_proj_list.append(
                nn.Sequential(
                    nn.Conv2d(in_channels, config.d_model, kernel_size=3, stride=2, padding=1),
                    nn.GroupNorm(32, config.d_model),
                )
            )
            in_channels = config.d_model
        self.input_proj_vision = nn.ModuleList(input_proj_list)

        # Create text backbone
        self.text_backbone = AutoModel.from_config(config.text_config, add_pooling_layer=False)
        self.text_projection = nn.Linear(config.text_config.hidden_size, config.d_model)

        if config.embedding_init_target or not config.two_stage:
            self.query_position_embeddings = nn.Embedding(config.num_queries, config.d_model)

        self.encoder = MMGroundingDinoEncoder(config)
        self.decoder = MMGroundingDinoDecoder(config)

        self.level_embed = nn.Parameter(torch.Tensor(config.num_feature_levels, config.d_model))

        self.enc_output = nn.Linear(config.d_model, config.d_model)
        self.enc_output_norm = nn.LayerNorm(config.d_model, config.layer_norm_eps)
        self.encoder_output_bbox_embed = MMGroundingDinoMLPPredictionHead(
            input_dim=config.d_model, hidden_dim=config.d_model, output_dim=4, num_layers=3
        )
        self.encoder_output_class_embed = MMGroundingDinoContrastiveEmbedding(config)

        self.post_init()


class MMGroundingDinoMLPPredictionHead(GroundingDinoMLPPredictionHead):
    pass


class MMGroundingDinoForObjectDetection(GroundingDinoForObjectDetection, MMGroundingDinoPreTrainedModel):
    _tied_weights_keys = {
        r"bbox_embed.(?![0])\d+": r"bbox_embed.0",
        r"class_embed.(?![0])\d+": r"^class_embed.0",
        "model.decoder.bbox_embed": "bbox_embed",
        "model.decoder.class_embed": "class_embed",
    }

    def __init__(self, config: MMGroundingDinoConfig):
        MMGroundingDinoPreTrainedModel.__init__(self, config)

        self.model = MMGroundingDinoModel(config)

        self.class_embed = nn.ModuleList(
            [MMGroundingDinoContrastiveEmbedding(config) for _ in range(config.decoder_layers)]
        )

        self.bbox_embed = nn.ModuleList(
            [
                MMGroundingDinoMLPPredictionHead(
                    input_dim=config.d_model, hidden_dim=config.d_model, output_dim=4, num_layers=3
                )
                for _ in range(config.decoder_layers)
            ]
        )
        # Initialize weights and apply final processing
        self.model.decoder.class_embed = self.class_embed  # class embed has no weights so nothing to tie
        self.model.decoder.bbox_embed = self.bbox_embed
        self.post_init()


__all__ = [
    "MMGroundingDinoConfig",
    "MMGroundingDinoForObjectDetection",
    "MMGroundingDinoModel",
    "MMGroundingDinoPreTrainedModel",
]
