# Copyright (c) OpenMMLab. All rights reserved.
from pathlib import Path
from typing import Callable, List, Optional, Union

import numpy as np
from mmcv.image import imread
from mmengine.config import Config
from mmengine.dataset import Compose, default_collate

from mmpretrain.registry import TRANSFORMS
from mmpretrain.structures import DataSample
from .base import BaseInferencer
from .model import list_models


class VisualGroundingInferencer(BaseInferencer):
    """The inferencer for visual grounding.

    Args:
        model (BaseModel | str | Config): A model name or a path to the config
            file, or a :obj:`BaseModel` object. The model name can be found
            by ``VisualGroundingInferencer.list_models()`` and you can also
            query it in :doc:`/modelzoo_statistics`.
        pretrained (str, optional): Path to the checkpoint. If None, it will
            try to find a pre-defined weight from the model you specified
            (only work if the ``model`` is a model name). Defaults to None.
        device (str, optional): Device to run inference. If None, the available
            device will be automatically used. Defaults to None.
        **kwargs: Other keyword arguments to initialize the model (only work if
            the ``model`` is a model name).

    Example:
        >>> from mmpretrain import VisualGroundingInferencer
        >>> inferencer = VisualGroundingInferencer('ofa-base_3rdparty_refcoco')
        >>> inferencer('demo/cat-dog.png', 'dog')[0]
        {'pred_bboxes': tensor([[ 36.6000,  29.6000, 355.8000, 395.2000]])}
    """  # noqa: E501

    visualize_kwargs: set = {
        'resize', 'show', 'show_dir', 'wait_time', 'line_width', 'bbox_color'
    }

    def __call__(self,
                 images: Union[str, np.ndarray, list],
                 texts: Union[str, list],
                 return_datasamples: bool = False,
                 batch_size: int = 1,
                 **kwargs) -> dict:
        """Call the inferencer.

        Args:
            images (str | array | list): The image path or array, or a list of
                images.
            texts (str | list): The text to do visual grounding.
            return_datasamples (bool): Whether to return results as
                :obj:`DataSample`. Defaults to False.
            batch_size (int): Batch size. Defaults to 1.
            resize (int, optional): Resize the short edge of the image to the
                specified length before visualization. Defaults to None.
            draw_score (bool): Whether to draw the prediction scores
                of prediction categories. Defaults to True.
            show (bool): Whether to display the visualization result in a
                window. Defaults to False.
            wait_time (float): The display time (s). Defaults to 0, which means
                "forever".
            show_dir (str, optional): If not None, save the visualization
                results in the specified directory. Defaults to None.
            line_width (int): The line width of the bbox. Defaults to 3.
            bbox_color (str | tuple): The color of the bbox.
                Defaults to 'green'.

        Returns:
            list: The inference results.
        """
        if not isinstance(images, (list, tuple)):
            assert isinstance(texts, str)
            inputs = [{'img': images, 'text': texts}]
        else:
            inputs = []
            for i in range(len(images)):
                input_ = {'img': images[i], 'text': texts[i]}
                inputs.append(input_)

        return super().__call__(inputs, return_datasamples, batch_size,
                                **kwargs)

    def _init_pipeline(self, cfg: Config) -> Callable:
        test_pipeline_cfg = cfg.test_dataloader.dataset.pipeline
        if test_pipeline_cfg[0]['type'] == 'LoadImageFromFile':
            # Image loading is finished in `self.preprocess`.
            test_pipeline_cfg = test_pipeline_cfg[1:]
        test_pipeline = Compose(
            [TRANSFORMS.build(t) for t in test_pipeline_cfg])
        return test_pipeline

    def preprocess(self, inputs: List[dict], batch_size: int = 1):

        def load_image(input_: dict):
            img = imread(input_['img'])
            if img is None:
                raise ValueError(f'Failed to read image {input_}.')
            return {**input_, 'img': img}

        pipeline = Compose([load_image, self.pipeline])

        chunked_data = self._get_chunk_data(map(pipeline, inputs), batch_size)
        yield from map(default_collate, chunked_data)

    def visualize(self,
                  ori_inputs: List[dict],
                  preds: List[DataSample],
                  show: bool = False,
                  wait_time: int = 0,
                  resize: Optional[int] = None,
                  line_width: int = 3,
                  bbox_color: Union[str, tuple] = 'green',
                  show_dir=None):
        if not show and show_dir is None:
            return None

        if self.visualizer is None:
            from mmpretrain.visualization import UniversalVisualizer
            self.visualizer = UniversalVisualizer()

        visualization = []
        for i, (input_, data_sample) in enumerate(zip(ori_inputs, preds)):
            image = imread(input_['img'])
            if isinstance(input_['img'], str):
                # The image loaded from path is BGR format.
                image = image[..., ::-1]
                name = Path(input_['img']).stem
            else:
                name = str(i)

            if show_dir is not None:
                show_dir = Path(show_dir)
                show_dir.mkdir(exist_ok=True)
                out_file = str((show_dir / name).with_suffix('.png'))
            else:
                out_file = None

            self.visualizer.visualize_visual_grounding(
                image,
                data_sample,
                resize=resize,
                show=show,
                wait_time=wait_time,
                line_width=line_width,
                bbox_color=bbox_color,
                name=name,
                out_file=out_file)
            visualization.append(self.visualizer.get_image())
        if show:
            self.visualizer.close()
        return visualization

    def postprocess(self,
                    preds: List[DataSample],
                    visualization: List[np.ndarray],
                    return_datasamples=False) -> dict:
        if return_datasamples:
            return preds

        results = []
        for data_sample in preds:
            results.append({'pred_bboxes': data_sample.get('pred_bboxes')})

        return results

    @staticmethod
    def list_models(pattern: Optional[str] = None):
        """List all available model names.

        Args:
            pattern (str | None): A wildcard pattern to match model names.

        Returns:
            List[str]: a list of model names.
        """
        return list_models(pattern=pattern, task='Visual Grounding')