import matplotlib.pyplot as plt
import numpy as np
import torch
from torchmetrics import CosineSimilarity
from PIL import Image
import skimage
from torch.backends import cudnn
import random
seed = 0
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
# cudnn.deterministic = True
# cudnn.benchmark = True


def decode_seg_map_sequence(label_masks, dataset='pascal'):
    rgb_masks = []
    for label_mask in label_masks:
        rgb_mask = decode_segmap(label_mask, dataset)
        rgb_masks.append(rgb_mask)
    rgb_masks = torch.from_numpy(np.array(rgb_masks).transpose([0, 3, 1, 2]))
    return rgb_masks


def decode_segmap(label_mask, dataset, plot=False):
    """Decode segmentation class labels into a color image
    Args:
        label_mask (np.ndarray): an (M,N) array of integer values denoting
          the class label at each spatial location.
        plot (bool, optional): whether to show the resulting color image
          in a figure.
    Returns:
        (np.ndarray, optional): the resulting decoded color image.
    """
    if dataset == 'pascal' or dataset == 'coco':
        n_classes = 21
        label_colours = get_cityscapes_labels()
    elif dataset == 'cityscapes':
        n_classes = 19
        label_colours = get_cityscapes_labels()
    else:
        raise NotImplementedError

    r = label_mask.copy()
    g = label_mask.copy()
    b = label_mask.copy()
    for ll in range(0, n_classes):
        r[label_mask == ll] = label_colours[ll, 0]
        g[label_mask == ll] = label_colours[ll, 1]
        b[label_mask == ll] = label_colours[ll, 2]
    rgb = np.zeros((label_mask.shape[0], label_mask.shape[1], 3))
    rgb[:, :, 0] = r / 255.0
    rgb[:, :, 1] = g / 255.0
    rgb[:, :, 2] = b / 255.0
    if plot:
        plt.imshow(rgb)
        plt.savefig('./CLIP_try.jpg')
        plt.show()
    else:
        return rgb


def encode_segmap(mask):
    """Encode segmentation label images as pascal classes
    Args:
        mask (np.ndarray): raw segmentation label image of dimension
          (M, N, 3), in which the Pascal classes are encoded as colours.
    Returns:
        (np.ndarray): class map with dimensions (M,N), where the value at
        a given location is the integer denoting the class index.
    """
    mask = mask.astype(int)
    label_mask = np.zeros((mask.shape[0], mask.shape[1]), dtype=np.int16)
    for ii, label in enumerate(get_cityscapes_labels()):
        label_mask[np.where(np.all(mask == label, axis=-1))[:2]] = ii
    label_mask = label_mask.astype(int)
    return label_mask


def get_cityscapes_labels():
    return np.array([
        [128, 64, 128],
        [244, 35, 232],
        [70, 70, 70],
        [102, 102, 156],
        [190, 153, 153],
        [153, 153, 153],
        [250, 170, 30],
        [220, 220, 0],
        [107, 142, 35],
        [152, 251, 152],
        [70, 130, 180],          # 0 not 70
        [220, 20, 60],
        [255, 0, 0],
        [0, 0, 142],
        [0, 0, 70],
        [0, 60, 100],
        [0, 80, 100],
        [0, 0, 230],
        [119, 11, 32]])

'''mask_path = '/four_tb/GTAV/labels/train/19349.png'
img_path = '/four_tb/GTAV/images/train/19349.png'
city_mask_path = '/home/user/Perception/Cityscapes/leftImg8bit_trainvaltest/gtFine_trainvaltest/gtFine/train/aachen/aachen_000000_000019_gtFine_color.png'
mask_pil = np.array(Image.open(city_mask_path),dtype=np.uint8)
mask_skimg = skimage.io.imread(city_mask_path)
img_skimg = skimage.io.imread(img_path)
#print(img_skimg.shape)
print(mask_skimg.shape)
#print(np.unique(mask_pil))
print(np.unique(mask_skimg))
print(np.unique(mask_pil))
print(hey)

def get_pascal_labels():
    """Load the mapping that associates pascal classes with label colors
    Returns:
        np.ndarray with dimensions (21, 3)
    """
    return np.asarray([[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0],
                       [0, 0, 128], [128, 0, 128], [0, 128, 128], [128, 128, 128],
                       [64, 0, 0], [192, 0, 0], [64, 128, 0], [192, 128, 0],
                       [64, 0, 128], [192, 0, 128], [64, 128, 128], [192, 128, 128],
                       [0, 64, 0], [128, 64, 0], [0, 192, 0], [128, 192, 0],
                       [0, 64, 128]])


#enc_mask_pil = encode_segmap(mask_pil)
enc_mask_skimg = encode_segmap(mask_skimg)
print(np.unique(enc_mask_skimg))
#print(np.unique(enc_mask_pil))


cosine_similarity = CosineSimilarity(reduction = 'mean')
def cosine_sim(feat1,feat2,feat3):
    #feat1 = torch.unsqueeze(feat1, dim=0)
    #feat1 = feat1.view(feat1.size(0),-1)
    feat2 = torch.unsqueeze(feat2, dim=0)
    feat2 = feat2.view(feat2.size(0),-1)
    #feat3 = torch.unsqueeze(feat3, dim=0)
    #feat3 = feat3.view(feat3.size(0),-1)
    feat4  = torch.ones(1,16,512,512)
    feat4 = feat4.view(feat4.size(0),-1)
    #f = open("cosineSim_Unet_and_Simpleunet.txt", "a")
    #f.write(str(cosine_similarity(feat1.cpu(),feat2.cpu()).item())+"\n")
    #f.close()
    #f = open("cosineSim_Unet_and_OC.txt", "a")
    #f.write(str(cosine_similarity(feat1.cpu(),feat3.cpu()).item())+"\n")
    #f.close()
    f = open("cosineSim_SimpleUnet_and_ones.txt", "a")
    f.write(str(cosine_similarity(feat2.cpu(),feat4.cpu()).item())+"\n")
    f.close()
    #f = open("cosineSim_OC_and_ones.txt", "a")
    #f.write(str(cosine_similarity(feat3.cpu(),feat4.cpu()).item())+"\n")
    #f.close()

    return
    

from collections import namedtuple


#--------------------------------------------------------------------------------
# Definitions
#--------------------------------------------------------------------------------

# a label and all meta information
Label = namedtuple( 'Label' , [

    'name'        , # The identifier of this label, e.g. 'car', 'person', ... .
                    # We use them to uniquely name a class

    'id'          , # An integer ID that is associated with this label.
                    # The IDs are used to represent the label in ground truth images
                    # An ID of -1 means that this label does not have an ID and thus
                    # is ignored when creating ground truth images (e.g. license plate).
                    # Do not modify these IDs, since exactly these IDs are expected by the
                    # evaluation server.

    'trainId'     , # Feel free to modify these IDs as suitable for your method. Then create
                    # ground truth images with train IDs, using the tools provided in the
                    # 'preparation' folder. However, make sure to validate or submit results
                    # to our evaluation server using the regular IDs above!
                    # For trainIds, multiple labels might have the same ID. Then, these labels
                    # are mapped to the same class in the ground truth images. For the inverse
                    # mapping, we use the label that is defined first in the list below.
                    # For example, mapping all void-type classes to the same ID in training,
                    # might make sense for some approaches.
                    # Max value is 255!

    'category'    , # The name of the category that this label belongs to

    'categoryId'  , # The ID of this category. Used to create ground truth images
                    # on category level.

    'hasInstances', # Whether this label distinguishes between single instances or not

    'ignoreInEval', # Whether pixels having this class as ground truth label are ignored
                    # during evaluations or not

    'color'       , # The color of this label
    ] )


#--------------------------------------------------------------------------------
# A list of all labels
#--------------------------------------------------------------------------------

# Please adapt the train IDs as appropriate for you approach.
# Note that you might want to ignore labels with ID 255 during training.
# Further note that the current train IDs are only a suggestion. You can use whatever you like.
# Make sure to provide your results using the original IDs and not the training IDs.
# Note that many IDs are ignored in evaluation and thus you never need to predict these!

labels = [
    #       name                     id    trainId   category            catId     hasInstances   ignoreInEval   color
    Label(  'unlabeled'            ,  0 ,      255 , 'void'            , 0       , False        , True         , (  0,  0,  0) ),
    Label(  'ego vehicle'          ,  1 ,      255 , 'void'            , 0       , False        , True         , (  0,  0,  0) ),
    Label(  'rectification border' ,  2 ,      255 , 'void'            , 0       , False        , True         , (  0,  0,  0) ),
    Label(  'out of roi'           ,  3 ,      255 , 'void'            , 0       , False        , True         , (  0,  0,  0) ),
    Label(  'static'               ,  4 ,      255 , 'void'            , 0       , False        , True         , (  0,  0,  0) ),
    Label(  'dynamic'              ,  5 ,      255 , 'void'            , 0       , False        , True         , (111, 74,  0) ),
    Label(  'ground'               ,  6 ,      255 , 'void'            , 0       , False        , True         , ( 81,  0, 81) ),
    Label(  'road'                 ,  7 ,        0 , 'flat'            , 1       , False        , False        , (128, 64,128) ),
    Label(  'sidewalk'             ,  8 ,        1 , 'flat'            , 1       , False        , False        , (244, 35,232) ),
    Label(  'parking'              ,  9 ,      255 , 'flat'            , 1       , False        , True         , (250,170,160) ),
    Label(  'rail track'           , 10 ,      255 , 'flat'            , 1       , False        , True         , (230,150,140) ),
    Label(  'building'             , 11 ,        2 , 'construction'    , 2       , False        , False        , ( 70, 70, 70) ),
    Label(  'wall'                 , 12 ,        3 , 'construction'    , 2       , False        , False        , (102,102,156) ),
    Label(  'fence'                , 13 ,        4 , 'construction'    , 2       , False        , False        , (190,153,153) ),
    Label(  'guard rail'           , 14 ,      255 , 'construction'    , 2       , False        , True         , (180,165,180) ),
    Label(  'bridge'               , 15 ,      255 , 'construction'    , 2       , False        , True         , (150,100,100) ),
    Label(  'tunnel'               , 16 ,      255 , 'construction'    , 2       , False        , True         , (150,120, 90) ),
    Label(  'pole'                 , 17 ,        5 , 'object'          , 3       , False        , False        , (153,153,153) ),
    Label(  'polegroup'            , 18 ,      255 , 'object'          , 3       , False        , True         , (153,153,154) ),   # (153,153,153)
    Label(  'traffic light'        , 19 ,        6 , 'object'          , 3       , False        , False        , (250,170, 30) ),
    Label(  'traffic sign'         , 20 ,        7 , 'object'          , 3       , False        , False        , (220,220,  0) ),
    Label(  'vegetation'           , 21 ,        8 , 'nature'          , 4       , False        , False        , (107,142, 35) ),
    Label(  'terrain'              , 22 ,        9 , 'nature'          , 4       , False        , False        , (152,251,152) ),
    Label(  'sky'                  , 23 ,       10 , 'sky'             , 5       , False        , False        , ( 70,130,180) ),
    Label(  'person'               , 24 ,       11 , 'human'           , 6       , True         , False        , (220, 20, 60) ),
    Label(  'rider'                , 25 ,       12 , 'human'           , 6       , True         , False        , (255,  0,  0) ),
    Label(  'car'                  , 26 ,       13 , 'vehicle'         , 7       , True         , False        , (  0,  0,142) ),
    Label(  'truck'                , 27 ,       14 , 'vehicle'         , 7       , True         , False        , (  0,  0, 70) ),
    Label(  'bus'                  , 28 ,       15 , 'vehicle'         , 7       , True         , False        , (  0, 60,100) ),
    Label(  'caravan'              , 29 ,      255 , 'vehicle'         , 7       , True         , True         , (  0,  0, 90) ),
    Label(  'trailer'              , 30 ,      255 , 'vehicle'         , 7       , True         , True         , (  0,  0,110) ),
    Label(  'train'                , 31 ,       16 , 'vehicle'         , 7       , True         , False        , (  0, 80,100) ),
    Label(  'motorcycle'           , 32 ,       17 , 'vehicle'         , 7       , True         , False        , (  0,  0,230) ),
    Label(  'bicycle'              , 33 ,       18 , 'vehicle'         , 7       , True         , False        , (119, 11, 32) ),
    Label(  'license plate'        , -1 ,       -1 , 'vehicle'         , 7       , False        , True         , (  0,  0,143) ),   # (  0,  0,142)
]
'''

