import json
import os
from os import path
from pathlib import Path
import shutil
import collections

import cv2
from PIL import Image
import torch

from util.image_loader import PaletteConverter

if not hasattr(Image, 'Resampling'):  # Pillow<9.0
    Image.Resampling = Image
import numpy as np

from util.palette import davis_palette
import progressbar
 

# https://bugs.python.org/issue28178
# ah python ah why
class LRU:
    def __init__(self, func, maxsize=128):
        self.cache = collections.OrderedDict()
        self.func = func
        self.maxsize = maxsize
 
    def __call__(self, *args):
        cache = self.cache
        if args in cache:
            cache.move_to_end(args)
            return cache[args]
        result = self.func(*args)
        cache[args] = result
        if len(cache) > self.maxsize:
            cache.popitem(last=False)
        return result

    def invalidate(self, key):
        self.cache.pop(key, None)


class ResourceManager:
    def __init__(self, config):
        # determine inputs
        images = config['images']
        video = config['video']
        self.workspace = config['workspace']
        self.size = config['size']
        self.palette = davis_palette
        self.palette_converter = PaletteConverter(self.palette)

        # create temporary workspace if not specified
        if self.workspace is None:
            if images is not None:
                p_images = Path(images)
                if p_images.name == 'JPEGImages' or (Path.cwd() / 'workspace') in p_images.parents:
                    # take the name instead of actual images dir (second case checks for videos already in ./workspace )
                    basename = p_images.parent.name
                else:
                    basename = p_images.name
            elif video is not None:
                basename = path.basename(video)[:-4]
            else:
                raise NotImplementedError(
                    'Either images, video, or workspace has to be specified')

            self.workspace = path.join('./workspace', basename)

        print(f'Workspace is in: {self.workspace}')
        self.workspace_info_file = path.join(self.workspace, 'info.json')
        self.references = set()
        self._num_objects = None
        self._try_load_info()

        if config['num_objects'] is not None:  # forced overwrite from user
            self._num_objects = config['num_objects']
        elif self._num_objects is None:  # both are None, single object first run use case
            self._num_objects = config['num_objects_default_value']
        self._save_info()

        # determine the location of input images
        need_decoding = False
        need_resizing = False
        if path.exists(path.join(self.workspace, 'images')):
            pass
        elif images is not None:
            need_resizing = True
        elif video is not None:
            # will decode video into frames later
            need_decoding = True

        # create workspace subdirectories
        self.image_dir = path.join(self.workspace, 'images')
        self.mask_dir = path.join(self.workspace, 'masks')
        os.makedirs(self.image_dir, exist_ok=True)
        os.makedirs(self.mask_dir, exist_ok=True)

        # convert read functions to be buffered
        self.get_image = LRU(self._get_image_unbuffered, maxsize=config['buffer_size'])
        self.get_mask = LRU(self._get_mask_unbuffered, maxsize=config['buffer_size'])

        # extract frames from video
        if need_decoding:
            self._extract_frames(video)

        # copy/resize existing images to the workspace
        if need_resizing:
            self._copy_resize_frames(images)

        # read all frame names
        self.names = sorted(os.listdir(self.image_dir))
        self.names = [f[:-4] for f in self.names] # remove extensions
        self.length = len(self.names)

        assert self.length > 0, f'No images found! Check {self.workspace}/images. Remove folder if necessary.'

        print(f'{self.length} images found.')

        self.height, self.width = self.get_image(0).shape[:2]
        self.visualization_init = False

        self._resize = None
        self._masks = None
        self._keys = None
        self._keys_processed = np.zeros(self.length, dtype=bool)
        self.key_h = None
        self.key_w = None

    def _extract_frames(self, video):
        cap = cv2.VideoCapture(video)
        frame_index = 0
        print(f'Extracting frames from {video} into {self.image_dir}...')
        bar = progressbar.ProgressBar(max_value=progressbar.UnknownLength)
        while(cap.isOpened()):
            _, frame = cap.read()
            if frame is None:
                break
            if self.size > 0:
                h, w = frame.shape[:2]
                new_w = (w*self.size//min(w, h))
                new_h = (h*self.size//min(w, h))
                if new_w != w or new_h != h:
                    frame = cv2.resize(frame,dsize=(new_w,new_h),interpolation=cv2.INTER_AREA)
            cv2.imwrite(path.join(self.image_dir, f'frame_{frame_index:06d}.jpg'), frame)
            frame_index += 1
            bar.update(frame_index)
        bar.finish()
        print('Done!')

    def _copy_resize_frames(self, images):
        image_list = os.listdir(images)
        print(f'Copying/resizing frames into {self.image_dir}...')
        for image_name in progressbar.progressbar(image_list):
            if self.size < 0:
                # just copy
                shutil.copy2(path.join(images, image_name), self.image_dir)
            else:
                frame = cv2.imread(path.join(images, image_name))
                h, w = frame.shape[:2]
                new_w = (w*self.size//min(w, h))
                new_h = (h*self.size//min(w, h))
                if new_w != w or new_h != h:
                    frame = cv2.resize(frame,dsize=(new_w,new_h),interpolation=cv2.INTER_AREA)
                cv2.imwrite(path.join(self.image_dir, image_name), frame)
        print('Done!')

    def add_key_and_stuff_with_mask(self, ti, key, shrinkage, selection, mask):
        if self._keys is None:
            c, h, w = key.squeeze().shape
            if self.key_h is None:
                self.key_h = h
            if self.key_w is None:
                self.key_w = w
            c_mask, h_mask, w_mask = mask.shape
            self._keys = torch.empty((self.length, c, h, w), dtype=key.dtype, device=key.device)
            self._shrinkages = torch.empty((self.length, 1, h, w), dtype=key.dtype, device=key.device)
            self._selections = torch.empty((self.length, c, h, w), dtype=key.dtype, device=key.device)
            self._masks = torch.empty((self.length, c_mask, h_mask, w_mask), dtype=mask.dtype, device=key.device)
            # self._resize = Resize((h, w), interpolation=InterpolationMode.NEAREST)
        
        if not self._keys_processed[ti]:
            # keys don't change for the video, so we only save them once
            self._keys[ti] = key
            self._shrinkages[ti] = shrinkage
            self._selections[ti] = selection
            self._keys_processed[ti] = True
                
        self._masks[ti] = mask# self._resize(mask)

    def all_masks_present(self):
        return self._keys_processed.sum() == self.length
    
    def add_reference(self, frame_id: int):
        self.references.add(frame_id)
        self._save_info()

    def remove_reference(self, frame_id: int):
        print(self.references)
        self.references.remove(frame_id)
        self._save_info()

    def _save_info(self):
        p_workspace_subdir = Path(self.workspace_info_file).parent
        p_workspace_subdir.mkdir(parents=True, exist_ok=True)
        with open(self.workspace_info_file, 'wt') as f:
            data = {'references': sorted(self.references), 'num_objects': self._num_objects}

            json.dump(data, f, indent=4)

    def _try_load_info(self):
        try:
            with open(self.workspace_info_file) as f:
                data = json.load(f)
                self._num_objects = data['num_objects']

                # We might have num_objects, but not references if imported the project
                self.references = set(data['references'])
        except Exception:
            pass


    def save_mask(self, ti, mask):
        # mask should be uint8 H*W without channels
        assert 0 <= ti < self.length
        assert isinstance(mask, np.ndarray)

        mask = Image.fromarray(mask)
        mask.putpalette(self.palette)
        mask.save(path.join(self.mask_dir, self.names[ti]+'.png'))
        self.invalidate(ti)

    def save_visualization(self, ti, image):
        # image should be uint8 3*H*W
        assert 0 <= ti < self.length
        assert isinstance(image, np.ndarray)
        if not self.visualization_init:
            self.visualization_dir = path.join(self.workspace, 'visualization')
            os.makedirs(self.visualization_dir, exist_ok=True)
            self.visualization_init = True

        image = Image.fromarray(image)
        image.save(path.join(self.visualization_dir, self.names[ti]+'.jpg'))

    def _get_image_unbuffered(self, ti):
        # returns H*W*3 uint8 array
        assert 0 <= ti < self.length

        image = Image.open(path.join(self.image_dir, self.names[ti]+'.jpg'))
        image = np.array(image)
        return image

    def _get_mask_unbuffered(self, ti):
        # returns H*W uint8 array
        assert 0 <= ti < self.length

        mask_path = path.join(self.mask_dir, self.names[ti]+'.png')
        if path.exists(mask_path):
            mask = Image.open(mask_path)
            mask = np.array(mask)
            return mask
        else:
            return None

    def read_external_image(self, file_name, size=None, force_mask=False):
        image = Image.open(file_name)
        is_mask = image.mode in ['L', 'P']

        if size is not None:
            # PIL uses (width, height)
            image = image.resize((size[1], size[0]), 
                    resample=Image.Resampling.NEAREST if is_mask or force_mask else Image.Resampling.BICUBIC)
        
        if force_mask and image.mode != 'P':
            image = self.palette_converter.image_to_index_mask(image)
        #     if image.mode in ['RGB', 'L'] and len(image.getcolors()) <= 2:
        #         image = np.array(image.convert('L'))
        #         # hardcoded for b&w images
        #         image = np.where(image, 1, 0)  # 255 (or whatever) -> binarize

        #         return image.astype('uint8')
        #     elif image.mode == 'RGB':
        #         image = image.convert('P', palette=self.palette)
        #         tmp_image = np.array(image)
        #         out_image = np.zeros_like(tmp_image)
        #         for i, c in enumerate(np.unique(tmp_image)):
        #             if i == 0:
        #                 continue
        #             out_image[tmp_image == c] = i  # palette indices into 0, 1, 2, ...
        #         self.palette = image.getpalette()
        #         return out_image
                
        #     image = image.convert('P', palette=self.palette)  # saved without DAVIS palette, just number objects 0, 1, ...
            
        image = np.array(image)
        return image

    def invalidate(self, ti):
        # the image buffer is never invalidated
        self.get_mask.invalidate((ti,))

    def __len__(self):
        return self.length

    @property
    def h(self):
        return self.height

    @property
    def w(self):
        return self.width
    
    @property
    def small_masks(self):
        return self._masks

    @property
    def keys(self):
        return self._keys
        

    @property
    def shrinkages(self):
        return self._shrinkages
    
    @property
    def selections(self):
        return self._selections
    
    @property
    def num_objects(self):
        return self._num_objects