import json
from shapely.geometry import shape
import os
import collections
from tqdm import tqdm
import pandas as pd  
from multiprocessing import cpu_count 
from multiprocessing.pool import ThreadPool
import requests
import networkx as nx
import rasterio as rio
from extract import extract
from tiling import get_tiles

from huggingface_hub import HfApi
from datetime import datetime
from tiling import get_tiles

def download_image(args): 
    url, fn = args['image_href'], args['image']

    if os.path.exists(fn) is False:
        try: 
            r = requests.get(url) 
            with open(fn, 'wb') as f:
                f.write(r.content) 
        except Exception as e: 
            print('Exception in download_url():', e)
            return url

    with rio.open(fn, "r") as ds:
        print(args['date'], args['id'], ds.crs.to_proj4())
    
    return url


if __name__ == '__main__':
    api = HfApi()

    image_dir = './dataset/image'

    if os.path.exists('data.json') == False:
        extract('data.json')
    
    with open('data.json') as f:
        data = json.load(f)


    ids = [f['id'] for f in data['features']]
    duplicated = [item for item, count in collections.Counter(ids).items() if count > 1]
    for duplicated_instance in duplicated:
        items = []
        for f in data['features']:
            if f['id'] == duplicated_instance:
                items.append(json.dumps(f))
        assert len(collections.Counter(items).keys()) == 1, 'Unexpected duplicated item' # Tutti gli elementi che hanno lo stesso id sono completamente identici

    # Prendo tutte le feature che sono univoce a livello di contenuto
    data['features'] =[json.loads(f) for f in list(set([json.dumps(f) for f in data['features']]))]
    #data['features'] = data['features'][:2]

    records = []
    for idx in tqdm(range(len(data['features']))):
        feature = data['features'][idx]

        gec = feature['assets'].get('GEC')
        if gec is None:
            continue


        metadata = feature['assets']['metadata']['content']
        assert len(metadata['collects']) == 1, 'Unexpected situation'
        assert len(metadata['derivedProducts']['GEC']) == 1, 'Unexpected situation'

        parsed_date = datetime.fromisoformat(metadata['collects'][0]['startAtUTC'])
        if parsed_date.year < 2024:
            continue
            

        records.append({
            'id': feature['id'],
            'date': metadata['collects'][0]['startAtUTC'],
            'bbox': feature['bbox'],
            'geometry': feature['geometry'],
            'satellite': metadata['umbraSatelliteName'],
            'track': metadata['collects'][0]['satelliteTrack'],
            'direction': metadata['collects'][0]['observationDirection'],
            'mode': metadata['imagingMode'],
            'band': metadata['collects'][0]['radarBand'],
            'polarization': metadata['collects'][0]['polarizations'],
            'azimuth_res': metadata['derivedProducts']['GEC'][0]['groundResolution']['azimuthMeters'],
            'range_res': metadata['derivedProducts']['GEC'][0]['groundResolution']['rangeMeters'],
            'rows': metadata['derivedProducts']['GEC'][0]['numRows'],
            'cols': metadata['derivedProducts']['GEC'][0]['numColumns'],
            'size': metadata['derivedProducts']['GEC'][0]['numRows']*metadata['derivedProducts']['GEC'][0]['numColumns'],
            'image_href': gec['href'],
            'image': os.path.join(image_dir, '{name}.tiff'.format(name=feature['id']))
        })



    cpus = cpu_count() 
    results = ThreadPool(cpus - 1).imap_unordered(download_image, records) 
    for result in results: 
        print('url:', result)

    for record in records:
        try:
            with rio.open(record['image']) as src:
                image_crs = src.crs.to_proj4()
                record['crs'] = src.crs.to_proj4()
        except:
            record['crs'] = 'None'
            print('Error reading the image')
    
    df = pd.DataFrame.from_records(records)
    df.to_excel('out.xlsx')



    selected_records = []
    for record in records:
        if record['crs'] == '+proj=longlat +datum=WGS84 +no_defs=True':
            out_dir = 'dataset/tile/{id}'.format(id = record['id'])
            if os.path.exists(out_dir) is False:
                os.mkdir(out_dir)
            selected_records.append({'input_path': record['image'], 'out_dir': out_dir, 'patch_size': 2048})
    cpus = cpu_count() 
    results = ThreadPool(cpus - 1).imap_unordered(get_tiles, selected_records) 
    for result in results: 
        print('url:', result)



    api.upload_file(
        path_or_fileobj='out.xlsx',
        path_in_repo='out.xlsx',
        repo_id='fedric95/umbra',
        repo_type='dataset',
    )

    api.upload_file(
        path_or_fileobj='data.json',
        path_in_repo='data.json',
        repo_id='fedric95/umbra',
        repo_type='dataset',
    )

    api.upload_large_folder(
        repo_id='fedric95/umbra',
        repo_type='dataset',
        folder_path='./dataset/',
    )