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

import numpy as np
import torch
import torch.nn.functional as F

from .typing_utils import SampleList


def add_prefix(inputs, prefix):
    """Add prefix for dict.

    Args:
        inputs (dict): The input dict with str keys.
        prefix (str): The prefix to add.

    Returns:

        dict: The dict with keys updated with ``prefix``.
    """

    outputs = dict()
    for name, value in inputs.items():
        outputs[f'{prefix}.{name}'] = value

    return outputs


def stack_batch(inputs: List[torch.Tensor],
                data_samples: Optional[SampleList] = None,
                size: Optional[tuple] = None,
                size_divisor: Optional[int] = None,
                pad_val: Union[int, float] = 0,
                seg_pad_val: Union[int, float] = 255) -> torch.Tensor:
    """Stack multiple inputs to form a batch and pad the images and gt_sem_segs
    to the max shape use the right bottom padding mode.

    Args:
        inputs (List[Tensor]): The input multiple tensors. each is a
            CHW 3D-tensor.
        data_samples (list[:obj:`SegDataSample`]): The list of data samples.
            It usually includes information such as `gt_sem_seg`.
        size (tuple, optional): Fixed padding size.
        size_divisor (int, optional): The divisor of padded size.
        pad_val (int, float): The padding value. Defaults to 0
        seg_pad_val (int, float): The padding value. Defaults to 255

    Returns:
       Tensor: The 4D-tensor.
       List[:obj:`SegDataSample`]: After the padding of the gt_seg_map.
    """
    assert isinstance(inputs, list), \
        f'Expected input type to be list, but got {type(inputs)}'
    assert len({tensor.ndim for tensor in inputs}) == 1, \
        f'Expected the dimensions of all inputs must be the same, ' \
        f'but got {[tensor.ndim for tensor in inputs]}'
    assert inputs[0].ndim == 3, f'Expected tensor dimension to be 3, ' \
        f'but got {inputs[0].ndim}'
    assert len({tensor.shape[0] for tensor in inputs}) == 1, \
        f'Expected the channels of all inputs must be the same, ' \
        f'but got {[tensor.shape[0] for tensor in inputs]}'

    # only one of size and size_divisor should be valid
    assert (size is not None) ^ (size_divisor is not None), \
        'only one of size and size_divisor should be valid'

    padded_inputs = []
    padded_samples = []
    inputs_sizes = [(img.shape[-2], img.shape[-1]) for img in inputs]
    max_size = np.stack(inputs_sizes).max(0)
    if size_divisor is not None and size_divisor > 1:
        # the last two dims are H,W, both subject to divisibility requirement
        max_size = (max_size +
                    (size_divisor - 1)) // size_divisor * size_divisor

    for i in range(len(inputs)):
        tensor = inputs[i]
        if size is not None:
            width = max(size[-1] - tensor.shape[-1], 0)
            height = max(size[-2] - tensor.shape[-2], 0)
            # (padding_left, padding_right, padding_top, padding_bottom)
            padding_size = (0, width, 0, height)
        elif size_divisor is not None:
            width = max(max_size[-1] - tensor.shape[-1], 0)
            height = max(max_size[-2] - tensor.shape[-2], 0)
            padding_size = (0, width, 0, height)
        else:
            padding_size = [0, 0, 0, 0]

        # pad img
        pad_img = F.pad(tensor, padding_size, value=pad_val)
        padded_inputs.append(pad_img)
        # pad gt_sem_seg
        if data_samples is not None:
            data_sample = data_samples[i]
            pad_shape = None
            if 'gt_sem_seg' in data_sample:
                gt_sem_seg = data_sample.gt_sem_seg.data
                del data_sample.gt_sem_seg.data
                data_sample.gt_sem_seg.data = F.pad(
                    gt_sem_seg, padding_size, value=seg_pad_val)
                pad_shape = data_sample.gt_sem_seg.shape
            if 'gt_edge_map' in data_sample:
                gt_edge_map = data_sample.gt_edge_map.data
                del data_sample.gt_edge_map.data
                data_sample.gt_edge_map.data = F.pad(
                    gt_edge_map, padding_size, value=seg_pad_val)
                pad_shape = data_sample.gt_edge_map.shape
            if 'gt_depth_map' in data_sample:
                gt_depth_map = data_sample.gt_depth_map.data
                del data_sample.gt_depth_map.data
                data_sample.gt_depth_map.data = F.pad(
                    gt_depth_map, padding_size, value=seg_pad_val)
                pad_shape = data_sample.gt_depth_map.shape
            data_sample.set_metainfo({
                'img_shape': tensor.shape[-2:],
                'pad_shape': pad_shape,
                'padding_size': padding_size
            })
            padded_samples.append(data_sample)
        else:
            padded_samples.append(
                dict(
                    img_padding_size=padding_size,
                    pad_shape=pad_img.shape[-2:]))

    return torch.stack(padded_inputs, dim=0), padded_samples


def multicrop_stack_batch(inputs: List[torch.Tensor],
                data_samples: Optional[SampleList] = None,
                ) -> torch.Tensor:
    assert isinstance(inputs, list), \
        f'Expected input type to be list, but got {type(inputs)}'
    assert len({tensor.ndim for tensor in inputs}) == 1, \
        f'Expected the dimensions of all inputs must be the same, ' \
        f'but got {[tensor.ndim for tensor in inputs]}'
    assert inputs[0].ndim == 4, f'Expected tensor dimension to be 4, ' \
        f'but got {inputs[0].ndim}'
    assert len({tensor.shape[1] for tensor in inputs}) == 1, \
        f'Expected the channels of all inputs must be the same, ' \
        f'but got {[tensor.shape[1] for tensor in inputs]}'


    trans_inputs = torch.stack(inputs, dim=0)
    tensor_shape = trans_inputs.shape
    levels = tensor_shape[1]
    batch_size = tensor_shape[0]
    ch, h, w = tensor_shape[2], tensor_shape[3], tensor_shape[4]
    trans_inputs = trans_inputs.reshape(batch_size*levels, ch, h, w)
    
    return trans_inputs, data_samples    
