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import functools

def lazy_property(func):
    ''' Caches the return value of a function, and turns it into a property. 
    
    Intended to be used as a function decorator::
    
        >>> class Foo:
        >>>     @lazy_property
        >>>     def bar(self):

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''' The parse_telegram_export function in this gist parses the html export of a Telegram chat.
You should have [BeautifulSoup](https://www.crummy.com/software/BeautifulSoup/bs4/) and 
[dateutil](https://dateutil.readthedocs.io/) installed. It extracts the sender name, the 
message date and time, the message text and the links in the message.
'''

from bs4 import BeautifulSoup
import dateutil

def parse_telegram_export(html_str, tz_name=None):

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import gi

gi.require_version('Gst', '1.0')
gi.require_version('GstRtspServer', '1.0')
from gi.repository import GLib, Gst, GstRtspServer

Gst.init(None)

import argparse
parser = argparse.ArgumentParser(description='GStreamer RTSP server test-launch')

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import panoramasdk
from backpack.annotation import (
    Point, LabelAnnotation, RectAnnotation, TimestampAnnotation,
    OpenCVImageAnnotationDriver, 
    PanoramaMediaAnnotationDriver
)

class Application(panoramasdk.node):

    def __init__(self):

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import panoramasdk
from backpack.kvs import KVSSpyGlass, KVSFileCredentialsHandler

# You might want to read these values from Panorama application parameters
stream_region = 'us-east-1'
stream_name = 'panorama-video'

# The example Dockerfile writes static configuration variables to this file
# If you change the .env file path in the Dockerfile, you should change it also here
DOTENV_PATH = '/panorama/.env'

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import os
import json
from pathlib import Path
from urllib.parse import urljoin

import jsonschema

def add_local_schemas_to(resolver, schema_folder, base_uri, schema_ext='.schema.json'):
    ''' Add local schema instances to a resolver schema cache. 
    

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class Application(panoramasdk.node):
    
    # ...
    
    def process_media(self, stream):
        image_data, ratio = self.preprocess(stream.image, self.MODEL_INPUT_SIZE)
        inference_results = self.call(
            {self.MODEL_INPUT_NAME: image_data}, self.MODEL_NODE
        )
        self.process_results(inference_results, stream, ratio)

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from yolox_postprocess import demo_postprocess, multiclass_nms

class Application(panoramasdk.node):

    # ...
    
    def process_results(self, inference_results, stream, ratio):
        media_height, media_width, _ = stream.image.shape
        media_scale = np.asarray([media_width, media_height, media_width, media_height])
        for output in inference_results:

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import cv2

class Application(panoramasdk.node):
    
    # ...

    def preprocess(self, img, input_size, swap=(2, 0, 1)):
        if len(img.shape) == 3:
            padded_img = np.ones((input_size[0], input_size[1], 3), dtype=np.uint8) * 114
        else: