import random
import string
import os

import requests
from tqdm import tqdm


def randomname(n):
   randlst = [random.choice(string.ascii_letters + string.digits) for i in range(n)]
   return ''.join(randlst)

def load_cn_model(model_dir):
  folder = model_dir
  file_name = 'diffusion_pytorch_model.safetensors'
  url = "https://huggingface.co/kataragi/ControlNet-LineartXL/resolve/main/Katarag_lineartXL-fp16.safetensors"

  file_path = os.path.join(folder, file_name)
  if not os.path.exists(file_path):
    response = requests.get(url, stream=True)

    total_size = int(response.headers.get('content-length', 0))
    with open(file_path, 'wb') as f, tqdm(
            desc=file_name,
            total=total_size,
            unit='iB',
            unit_scale=True,
            unit_divisor=1024,
        ) as bar:
        for data in response.iter_content(chunk_size=1024):
            size = f.write(data)
            bar.update(size)

def load_cn_config(model_dir):
  folder = model_dir
  file_name = 'config.json'
  url = "https://huggingface.co/mattyamonaca/controlnet_line2line_xl/resolve/main/config.json"

  file_path = os.path.join(folder, file_name)
  if not os.path.exists(file_path):
    response = requests.get(url, stream=True)

    total_size = int(response.headers.get('content-length', 0))
    with open(file_path, 'wb') as f, tqdm(
            desc=file_name,
            total=total_size,
            unit='iB',
            unit_scale=True,
            unit_divisor=1024,
        ) as bar:
        for data in response.iter_content(chunk_size=1024):
            size = f.write(data)
            bar.update(size)