import os
import json
import shutil
import datasets
import tifffile

import pandas as pd
import numpy as np
import geopandas as gpd

from datetime import datetime
from GFMBench.datasets.base_dataset import GFMBenchDataset

S2_MEAN = [1180.2278549 , 1387.76882557, 1436.67627781, 1773.66437066, 2735.86417202, 3080.12530686, 3223.60015887, 3338.35639825, 2418.01390106, 1630.11250759]

S2_STD = [1976.91493068, 1917.02121286, 1996.45123112, 1903.34296117, 1785.08356262, 1796.4477813 , 1811.90019014, 1793.47036145, 1474.46979658, 1309.88416505]

S1A_MEAN = [-10.91848081, -17.34320436]

S1A_STD = [3.26830557, 3.19895575]

S1D_MEAN = [-11.07395082, -17.45261358]

S1D_STD = [3.33774017, 3.15584225]

S1_MEAN = [-10.996215815 -17.39790897]

S1_STD = [3.30411987, 3.177943]

s1_metadata = {
    'radar': {
        'mean': S1_MEAN,
        'std': S1_STD,
    },
    'radar_a': {
        'mean': S1A_MEAN,
        'std': S1A_STD,
    },
    'radar_d': {
        'mean': S1D_MEAN,
        'std': S1D_STD,
    },
}

s1_num_seq = {
    'radar': 142,
    'radar_a': 71,
    'radar_d': 71,
}

sats = {
    "radar": ["S2", "S1A", "S1D"],
    "radar_a": ["S2", "S1A"],
    "radar_d": ["S2", "S1D"],
}

class PASTISDataset(GFMBenchDataset):
    VERSION = datasets.Version("1.0.0")
    
    DATA_URL = "https://huggingface.co/datasets/GFM-Bench/PASTIS/resolve/main/PASTIS.tar.xz"

    metadata = {
        "s2c": {
            "bands": ["B2", "B3", "B4", "B5", "B6", "B7", "B8", "B8A", "B11", "B12"],
            "channel_wv": [492.4, 559.8, 664.6, 704.1, 740.5, 782.8, 832.8, 864.7, 1613.7, 2202.4],
            "mean": S2_MEAN,
            "std": S2_STD,
        },
        "s1": {
            "bands": ["VV", "VH"],
            "channel_wv": [5500, 5700],
        }
    }

    SIZE = HEIGHT = WIDTH = 128

    spatial_resolution = 10 

    NUM_CLASSES = 20 # 0 is background class, and 19 is the void label


    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="default"),
        *[datasets.BuilderConfig(name=name) for name in ['radar', 'radar_a', 'radar_d']]
    ] 

    DEFAULT_CONFIG_NAME = "radar"

    def __init__(self, reference_date="2018-09-10", **kwargs):
        name = kwargs.get('config_name', None)
        print(f"config_name: {name}")

        self.reference_date = datetime(*map(int, reference_date.split("-")))
        print(f"reference_date: {reference_date} -> {self.reference_date}")

        config = "radar" if name == "default" or name is None else name
        self.NUM_RADAR_SEQ = s1_num_seq[config]
        self.sats = sats[config]
        self.metadata["s1"].update(s1_metadata[config])
        self.sats_name = config

        super().__init__( **kwargs)

    def _info(self):
        metadata = self.metadata
        metadata['size'] = self.SIZE
        metadata['num_classes'] = self.NUM_CLASSES
        metadata['spatial_resolution'] = self.spatial_resolution

        return datasets.DatasetInfo(
            description=json.dumps(metadata),
            features=datasets.Features({
                "optical": datasets.Array4D(shape=(61, 10, self.HEIGHT, self.WIDTH), dtype="float32"),
                "radar": datasets.Array4D(shape=(self.NUM_RADAR_SEQ, 2, self.HEIGHT, self.WIDTH), dtype="float32"),
                "label": datasets.Array2D(shape=(self.HEIGHT, self.WIDTH), dtype="int32"),
                "optical_dates": datasets.Sequence(datasets.Value("int32")),
                "radar_dates": datasets.Sequence(datasets.Value("int32")),
                "optical_sequence_len": datasets.Value("int32"),
                "radar_sequence_len": datasets.Value("int32"),
                "optical_channel_wv": datasets.Sequence(datasets.Value("float32")),
                "radar_channel_wv": datasets.Sequence(datasets.Value("float32")),
                "spatial_resolution": datasets.Value("int32"),
            }),
        )
    
    def _split_generators(self, dl_manager):
        if isinstance(self.DATA_URL, list):
            downloaded_files = dl_manager.download(self.DATA_URL)
            combined_file = os.path.join(dl_manager.download_config.cache_dir, "combined.tar.gz")        
            with open(combined_file, 'wb') as outfile:
                for part_file in downloaded_files:
                    with open(part_file, 'rb') as infile:
                        shutil.copyfileobj(infile, outfile)
            data_dir = dl_manager.extract(combined_file)
            os.remove(combined_file)
        else:
            data_dir = dl_manager.download_and_extract(self.DATA_URL)

        return [
            datasets.SplitGenerator(
                name="train",
                gen_kwargs={
                    "split": 'train',
                    "data_dir": data_dir, 
                },
            ),
            datasets.SplitGenerator(
                name="val",
                gen_kwargs={
                    "split": 'val',
                    "data_dir": data_dir,
                },
            ),
            datasets.SplitGenerator(
                name="test",
                gen_kwargs={
                    "split": 'test',
                    "data_dir": data_dir,
                },
            )
        ]

    def _generate_examples(self, split, data_dir):
        optical_channel_wv = self.metadata["s2c"]["channel_wv"]
        radar_channel_wv = self.metadata["s1"]["channel_wv"]
        spatial_resolution = self.spatial_resolution

        data_dir = os.path.join(data_dir, "PASTIS")
        metadata = pd.read_csv(os.path.join(data_dir, "metadata.csv"))
        metadata = metadata[metadata["split"] == split].reset_index(drop=True)

        self._prepare_meta_patch(data_dir)
        self._prepare_date_tables()

        for index, row in metadata.iterrows():
            id_patch = row.optical_path.replace("DATA_S2/S2_", "").replace(".tif", "")

            optical_path = os.path.join(data_dir, row.optical_path)
            optical = self._read_image(optical_path).astype(np.float32) # TxCxHxW
            optical_sequence_len = optical.shape[0]
            optical = self._pad_sequence(optical, sat="S2") # 61xCxHxW
            optical_dates = self._get_dates(id_patch=id_patch, sat="S2")

            radar_sequence_len = 0
            if self.sats_name in ["radar", "radar_a"]:
                radar_a_path = os.path.join(data_dir, row.radar_a_path)
                radar_a = self._read_image(radar_a_path).astype(np.float32)[:, :2, :, :] # T, 2, 128, 128
                radar_a_dates = self._get_dates(id_patch=id_patch, sat="S1A")
                radar_sequence_len += radar_a.shape[0]
                if self.sats_name == "radar_a":
                    radar = self._pad_sequence(radar_a, "S1A") # 71, 2, 128, 128
                    radar_dates = radar_a_dates
            
            if self.sats_name in ["radar", "radar_d"]:
                radar_d_path = os.path.join(data_dir, row.radar_d_path)
                radar_d = self._read_image(radar_d_path).astype(np.float32)[:, :2, :, :]
                radar_d_dates = self._get_dates(id_patch=id_patch, sat="S1D")
                radar_sequence_len += radar_d.shape[0]
                if self.sats_name == "radar_d":
                    radar = self._pad_sequence(radar_d, sat="S1D") # 71, 2, 128, 128
                    radar_dates = radar_d_dates

            if self.sats_name == "radar":
                assert radar_a is not None and radar_d is not None
                radar, radar_dates = self._merge_sort_dates(radar_a_dates, radar_d_dates, radar_a, radar_d)
                radar = self._pad_sequence(radar, sat="S1_both") # 142, 2, 128, 128

            label_path = os.path.join(data_dir, row.label_path) # 3xHxW
            label = tifffile.imread(label_path)[0] # HxW

            sample = {
                "optical": optical,
                "optical_channel_wv": optical_channel_wv,
                "optical_dates": optical_dates,
                "optical_sequence_len": optical_sequence_len,
                "radar": radar,
                "radar_channel_wv": radar_channel_wv,
                "radar_dates": radar_dates,
                "radar_sequence_len": radar_sequence_len,
                "label": label,
                "spatial_resolution": spatial_resolution,
            }

            yield f"{index}", sample

    # util functions
    def _prepare_meta_patch(self, data_dir):
        self.meta_patch = gpd.read_file(os.path.join(data_dir, "metadata.geojson"))
        self.meta_patch.index = self.meta_patch["ID_PATCH"].astype(int)
        self.meta_patch.sort_index(inplace=True)

    def _prepare_date_tables(self):
        self.date_tables = {sat: None for sat in self.sats}
        self.date_range = np.array(range(-200, 600))
        for s in self.sats:
            dates = self.meta_patch["dates-{}".format(s)]
            date_table = pd.DataFrame(
                index=self.meta_patch.index, columns=self.date_range, dtype=int
            )
            for pid, date_seq in dates.items():
                if type(date_seq) == str:
                    date_seq = json.loads(date_seq)
                d = pd.DataFrame().from_dict(date_seq, orient="index")
                d = d[0].apply(
                    lambda x: (
                        datetime(int(str(x)[:4]), int(str(x)[4:6]), int(str(x)[6:]))
                        - self.reference_date
                    ).days
                )
                date_table.loc[pid, d.values] = 1
            date_table = date_table.fillna(0)
            self.date_tables[s] = {
                index: np.array(list(d.values()))
                for index, d in date_table.to_dict(orient="index").items()
            }
    
    def _get_dates(self, id_patch, sat="S2"):
        id_patch = int(id_patch)
        return self.date_range[np.where(self.date_tables[sat][id_patch] == 1)[0]]

    def _merge_sort_dates(self, radar_a_dates, radar_d_dates, radar_a, radar_d):
        merged_dates = np.concatenate((radar_a_dates, radar_d_dates))
        sorted_indices = np.argsort(merged_dates)

        sorted_images = np.concatenate((radar_a, radar_d), axis=0)[sorted_indices]
        sorted_dates = merged_dates[sorted_indices]

        return sorted_images, sorted_dates
    
    def _pad_sequence(self, image, sat="S2"):
        assert sat in ["S2", "S1A", "S1D", "S1_both"]
        sizes = {"S2": 61, "S1A": 71, "S1D": 71, "S1_both": 142}
        assert image.shape[0] <= sizes[sat]
        padding_size = sizes[sat] - image.shape[0]
        if padding_size == 0:
            return image
        
        pad = np.zeros((padding_size, *image.shape[1:]))
        padded_image = np.concatenate((image, pad), axis=0)
        return padded_image