import io
import os
import sys

import numpy as np
import PIL
import requests
import torch
import torch.nn.functional as F
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image, ImageDraw, ImageFont


def download_image(url):
    resp = requests.get(url)
    resp.raise_for_status()
    return PIL.Image.open(io.BytesIO(resp.content))


def preprocess(img, target_image_size=256, map_dalle=False):
    s = min(img.size)
    
    if s < target_image_size:
        raise ValueError(f'min dim for image {s} < {target_image_size}')
        
    r = target_image_size / s
    s = (round(r * img.size[1]), round(r * img.size[0]))
    img = TF.resize(img, s, interpolation=PIL.Image.LANCZOS)
    img = TF.center_crop(img, output_size=2 * [target_image_size])
    img = torch.unsqueeze(T.ToTensor()(img), 0)
    return img

def preprocess_vqgan(x):
  x = 2. * x - 1.
  return x

def custom_to_pil(x, process=True, mode="RGB"):
  x = x.detach().cpu()
  if process:
    x = torch.clamp(x, -1., 1.)
    x = (x + 1.)/2.
  x = x.permute(1,2,0).numpy()
  if process:
    x = (255*x).astype(np.uint8)
  x = Image.fromarray(x)
  if not x.mode == mode:
    x = x.convert(mode)
  return x

def get_pil(x):
  x = torch.clamp(x, -1., 1.)
  x = (x + 1.)/2.
  x = x.permute(1,2,0)
  return x

def loop_post_process(x):
  x = get_pil(x.squeeze())
  return x.permute(2, 0, 1).unsqueeze(0)

def stack_reconstructions(input, x0, x1, x2, x3, titles=[]):
  assert input.size == x1.size == x2.size == x3.size
  w, h = input.size[0], input.size[1]
  img = Image.new("RGB", (5*w, h))
  img.paste(input, (0,0))
  img.paste(x0, (1*w,0))
  img.paste(x1, (2*w,0))
  img.paste(x2, (3*w,0))
  img.paste(x3, (4*w,0))
  for i, title in enumerate(titles):
    ImageDraw.Draw(img).text((i*w, 0), f'{title}', (255, 255, 255), font=font) # coordinates, text, color, font
  return img