# Last modified: 2024-04-30
#
# Copyright 2023 Bingxin Ke, ETH Zurich. All rights reserved.
#
# 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.
# --------------------------------------------------------------------------
# If you find this code useful, we kindly ask you to cite our paper in your work.
# Please find bibtex at: https://github.com/prs-eth/Marigold#-citation
# If you use or adapt this code, please attribute to https://github.com/prs-eth/marigold.
# More information about the method can be found at https://marigoldmonodepth.github.io
# --------------------------------------------------------------------------
import glob
import io
import json
import os
import pdb
import random
import tarfile
from enum import Enum
from typing import Union

import numpy as np
import torch
from PIL import Image
from torch.utils.data import Dataset
from torchvision.transforms import InterpolationMode, Resize, CenterCrop
import torchvision.transforms as transforms
from transformers import CLIPTextModel, CLIPTokenizer
from src.util.depth_transform import DepthNormalizerBase
import random

from src.dataset.eval_base_dataset import DatasetMode, DepthFileNameMode
from pycocotools import mask as coco_mask
from scipy.ndimage import gaussian_filter

def read_image_from_tar(tar_obj, img_rel_path):
    image = tar_obj.extractfile("./" + img_rel_path)
    image = image.read()
    image = Image.open(io.BytesIO(image))


class BaseInpaintDataset(Dataset):
    def __init__(
        self,
        mode: DatasetMode,
        filename_ls_path: str,
        dataset_dir: str,
        disp_name: str,
        depth_transform: Union[DepthNormalizerBase, None] = None,
        tokenizer: CLIPTokenizer = None,
        augmentation_args: dict = None,
        resize_to_hw=None,
        move_invalid_to_far_plane: bool = True,
        rgb_transform=lambda x: x / 255.0 * 2 - 1,  #  [0, 255] -> [-1, 1],
        **kwargs,
    ) -> None:
        super().__init__()
        self.mode = mode
        # dataset info
        self.filename_ls_path = filename_ls_path
        self.disp_name = disp_name
        # training arguments
        self.depth_transform: DepthNormalizerBase = depth_transform
        self.augm_args = augmentation_args
        self.resize_to_hw = resize_to_hw
        self.rgb_transform = rgb_transform
        self.move_invalid_to_far_plane = move_invalid_to_far_plane
        self.tokenizer = tokenizer
        # Load filenames
        self.filenames = []
        filename_paths = glob.glob(self.filename_ls_path)
        for path in filename_paths:
            with open(path, "r") as f:
                self.filenames += json.load(f)
        # Tar dataset
        self.tar_obj = None
        self.is_tar = (
            True
            if os.path.isfile(dataset_dir) and tarfile.is_tarfile(dataset_dir)
            else False
        )

    def __len__(self):
        return len(self.filenames)

    def __getitem__(self, index):
        rasters, other = self._get_data_item(index)
        if DatasetMode.TRAIN == self.mode:
            rasters = self._training_preprocess(rasters)
        # merge
        outputs = rasters
        outputs.update(other)
        return outputs

    def _get_data_item(self, index):
        rgb_path = self.filenames[index]['rgb_path']
        mask_path = None
        if 'valid_mask' in self.filenames[index]:
            mask_path = self.filenames[index]['valid_mask']
        if self.filenames[index]['caption'] is not None:
            coca_caption = self.filenames[index]['caption']['coca_caption']
            spatial_caption = self.filenames[index]['caption']['spatial_caption']
            empty_caption = ''
            caption_choices = [coca_caption, spatial_caption, empty_caption]
            probabilities = [0.4, 0.4, 0.2]
            caption = random.choices(caption_choices, probabilities)[0]
        else:
            caption = ''

        rasters = {}
        # RGB data
        rasters.update(self._load_rgb_data(rgb_path))

        try:
            anno = json.load(open(rgb_path.replace('.jpg', '.json')))['annotations']
            random.shuffle(anno)
            object_num = random.randint(5, 10)
            mask = np.array(coco_mask.decode(anno[0]['segmentation']), dtype=np.uint8)
            for single_anno in (anno[0:object_num] if len(anno)>object_num else anno):
                mask += np.array(coco_mask.decode(single_anno['segmentation']), dtype=np.uint8)
        except:
            mask = None

        a = random.random()
        if a < 0.1 or mask is None:
            mask = np.zeros(rasters['rgb_int'].shape[-2:])
            rows, cols = mask.shape
            grid_size = random.randint(5, 14)
            grid_rows, grid_cols = rows // grid_size, cols // grid_size
            for i in range(grid_rows):
                for j in range(grid_cols):
                    random_prob = np.random.rand()
                    if random_prob < 0.2:
                        row_start = i * grid_size
                        row_end = (i + 1) * grid_size
                        col_start = j * grid_size
                        col_end = (j + 1) * grid_size
                        mask[row_start:row_end, col_start:col_end] = 1

        rasters['mask'] = torch.from_numpy(mask).unsqueeze(0).to(torch.float32)

        if self.resize_to_hw is not None:
            resize_transform = transforms.Compose([
                Resize(size=max(self.resize_to_hw), interpolation=InterpolationMode.NEAREST_EXACT),
                CenterCrop(size=self.resize_to_hw)])
            rasters = {k: resize_transform(v) for k, v in rasters.items()}

        # mask = torch.zeros(rasters['rgb_int'].shape[-2:])
        # rows, cols = mask.shape
        # grid_size = random.randint(3, 10)
        # grid_rows, grid_cols = rows // grid_size, cols // grid_size
        # for i in range(grid_rows):
        #     for j in range(grid_cols):
        #         random_prob = np.random.rand()
        #         if random_prob < 0.5:
        #             row_start = i * grid_size
        #             row_end = (i + 1) * grid_size
        #             col_start = j * grid_size
        #             col_end = (j + 1) * grid_size
        #             mask[row_start:row_end, col_start:col_end] = 1

        # rasters['mask'] = mask.unsqueeze(0)

        other = {"index": index, "rgb_path": rgb_path, 'text': caption}
        return rasters, other

    def _load_rgb_data(self, rgb_path):
        # Read RGB data
        rgb = self._read_rgb_file(rgb_path)
        rgb_norm = rgb / 255.0 * 2.0 - 1.0  #  [0, 255] -> [-1, 1]

        outputs = {
            "rgb_int": torch.from_numpy(rgb).int(),
            "rgb_norm": torch.from_numpy(rgb_norm).float(),
        }
        return outputs

    def _get_data_path(self, index):
        filename_line = self.filenames[index]

        # Get data path
        rgb_rel_path = filename_line[0]

        depth_rel_path, text_rel_path = None, None
        if DatasetMode.RGB_ONLY != self.mode:
            depth_rel_path = filename_line[1]
            if len(filename_line) > 2:
                text_rel_path = filename_line[2]
        return rgb_rel_path, depth_rel_path, text_rel_path

    def _read_image(self, img_path) -> np.ndarray:
        image_to_read = img_path
        image = Image.open(image_to_read)  # [H, W, rgb]
        image = np.asarray(image)
        return image

    def _read_rgb_file(self, path) -> np.ndarray:
        rgb = self._read_image(path)
        rgb = np.transpose(rgb, (2, 0, 1)).astype(int)  # [rgb, H, W]
        return rgb

    def _read_depth_file(self, path):
        depth_in = self._read_image(path)
        #  Replace code below to decode depth according to dataset definition
        depth_decoded = depth_in
        return depth_decoded

    def _training_preprocess(self, rasters):
        # Augmentation
        if self.augm_args is not None:
            rasters = self._augment_data(rasters)

        # Normalization
        # rasters["depth_raw_norm"] = rasters["depth_raw_linear"] / 255.0 * 2.0 - 1.0
        # rasters["depth_filled_norm"] = rasters["depth_filled_linear"] / 255.0 * 2.0 - 1.0

        rasters["depth_raw_norm"] = self.depth_transform(
            rasters["depth_raw_linear"], rasters["valid_mask_raw"]
        ).clone()
        rasters["depth_filled_norm"] = self.depth_transform(
            rasters["depth_filled_linear"], rasters["valid_mask_filled"]
        ).clone()

        # Set invalid pixel to far plane
        if self.move_invalid_to_far_plane:
            if self.depth_transform.far_plane_at_max:
                rasters["depth_filled_norm"][~rasters["valid_mask_filled"]] = (
                    self.depth_transform.norm_max
                )
            else:
                rasters["depth_filled_norm"][~rasters["valid_mask_filled"]] = (
                    self.depth_transform.norm_min
                )

        # Resize
        if self.resize_to_hw is not None:
            resize_transform = transforms.Compose([
                Resize(size=max(self.resize_to_hw), interpolation=InterpolationMode.NEAREST_EXACT),
                CenterCrop(size=self.resize_to_hw)])
            rasters = {k: resize_transform(v) for k, v in rasters.items()}
        return rasters

    def _augment_data(self, rasters_dict):
        # lr flipping
        lr_flip_p = self.augm_args.lr_flip_p
        if random.random() < lr_flip_p:
            rasters_dict = {k: v.flip(-1) for k, v in rasters_dict.items()}

        return rasters_dict

    def __del__(self):
        if hasattr(self, "tar_obj") and self.tar_obj is not None:
            self.tar_obj.close()
            self.tar_obj = None

def get_pred_name(rgb_basename, name_mode, suffix=".png"):
    if DepthFileNameMode.rgb_id == name_mode:
        pred_basename = "pred_" + rgb_basename.split("_")[1]
    elif DepthFileNameMode.i_d_rgb == name_mode:
        pred_basename = rgb_basename.replace("_rgb.", "_pred.")
    elif DepthFileNameMode.id == name_mode:
        pred_basename = "pred_" + rgb_basename
    elif DepthFileNameMode.rgb_i_d == name_mode:
        pred_basename = "pred_" + "_".join(rgb_basename.split("_")[1:])
    else:
        raise NotImplementedError
    # change suffix
    pred_basename = os.path.splitext(pred_basename)[0] + suffix

    return pred_basename