content: import importlib
  
  import random
  import torch
  import numpy as np
  from collections import abc
  
  import multiprocessing as mp
  from threading import Thread
  from queue import Queue
  
  from inspect import isfunction
  from PIL import Image, ImageDraw, ImageFont
  
  
  def log_txt_as_img(wh, xc, size=10):
      # wh a tuple of (width, height)
      # xc a list of captions to plot
      b = len(xc)
      txts = list()
      for bi in range(b):
          txt = Image.new("RGB", wh, color="white")
          draw = ImageDraw.Draw(txt)
          font = ImageFont.truetype("data/DejaVuSans.ttf", size=size)
          nc = int(40 * (wh[0] / 256))
          lines = "\n".join(
              xc[bi][start : start + nc] for start in range(0, len(xc[bi]), nc)
          )
  
          try:
              draw.text((0, 0), lines, fill="black", font=font)
          except UnicodeEncodeError:
              print("Cant encode string for logging. Skipping.")
  
          txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0
          txts.append(txt)
      txts = np.stack(txts)
      txts = torch.tensor(txts)
      return txts
  
  
  def ismap(x):
      if not isinstance(x, torch.Tensor):
          return False
      return (len(x.shape) == 4) and (x.shape[1] > 3)
  
  
  def isimage(x):
      if not isinstance(x, torch.Tensor):
          return False
      return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)
  
  
  def exists(x):
      return x is not None
  
  
  def default(val, d):
      if exists(val):
          return val
      return d() if isfunction(d) else d
  
  
  def mean_flat(tensor):
      """
      https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86
      Take the mean over all non-batch dimensions.
      """
      return tensor.mean(dim=list(range(1, len(tensor.shape))))
  
  
  def count_params(model, verbose=False):
      total_params = sum(p.numel() for p in model.parameters())
      if verbose:
          print(f"{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.")
      return total_params
  
  
  def instantiate_from_config(config):
      if not "target" in config:
          if config == "__is_first_stage__":
              return None
          elif config == "__is_unconditional__":
              return None
          raise KeyError("Expected key `target` to instantiate.")
      # import pdb; pdb.set_trace()
      return get_obj_from_str(config["target"])(**config.get("params", dict()))
  
  
  def get_obj_from_str(string, reload=False):
      module, cls = string.rsplit(".", 1)
      # import pdb; pdb.set_trace()
      if reload:
          module_imp = importlib.import_module(module)
          importlib.reload(module_imp)
      return getattr(importlib.import_module(module, package=None), cls)
  
  
  def _do_parallel_data_prefetch(func, Q, data, idx, idx_to_fn=False):
      # create dummy dataset instance
  
      # run prefetching
      if idx_to_fn:
          res = func(data, worker_id=idx)
      else:
          res = func(data)
      Q.put([idx, res])
      Q.put("Done")
  
  
  def parallel_data_prefetch(
      func: callable,
      data,
      n_proc,
      target_data_type="ndarray",
      cpu_intensive=True,
      use_worker_id=False,
  ):
      # if target_data_type not in ["ndarray", "list"]:
      #     raise ValueError(
      #         "Data, which is passed to parallel_data_prefetch has to be either of type list or ndarray."
      #     )
      if isinstance(data, np.ndarray) and target_data_type == "list":
          raise ValueError("list expected but function got ndarray.")
      elif isinstance(data, abc.Iterable):
          if isinstance(data, dict):
              print(
                  f'WARNING:"data" argument passed to parallel_data_prefetch is a dict: Using only its values and disregarding keys.'
              )
              data = list(data.values())
          if target_data_type == "ndarray":
              data = np.asarray(data)
          else:
              data = list(data)
      else:
          raise TypeError(
              f"The data, that shall be processed parallel has to be either an np.ndarray or an Iterable, but is actually {type(data)}."
          )
  
      if cpu_intensive:
          Q = mp.Queue(1000)
          proc = mp.Process
      else:
          Q = Queue(1000)
          proc = Thread
      # spawn processes
      if target_data_type == "ndarray":
          arguments = [
              [func, Q, part, i, use_worker_id]
              for i, part in enumerate(np.array_split(data, n_proc))
          ]
      else:
          step = (
              int(len(data) / n_proc + 1)
              if len(data) % n_proc != 0
              else int(len(data) / n_proc)
          )
          arguments = [
              [func, Q, part, i, use_worker_id]
              for i, part in enumerate(
                  [data[i : i + step] for i in range(0, len(data), step)]
              )
          ]
      processes = []
      for i in range(n_proc):
          p = proc(target=_do_parallel_data_prefetch, args=arguments[i])
          processes += [p]
  
      # start processes
      print(f"Start prefetching...")
      import time
  
      start = time.time()
      gather_res = [[] for _ in range(n_proc)]
      try:
          for p in processes:
              p.start()
  
          k = 0
          while k < n_proc:
              # get result
              res = Q.get()
              if res == "Done":
                  k += 1
              else:
                  gather_res[res[0]] = res[1]
  
      except Exception as e:
          print("Exception: ", e)
          for p in processes:
              p.terminate()
  
          raise e
      finally:
          for p in processes:
              p.join()
          print(f"Prefetching complete. [{time.time() - start} sec.]")
  
      if target_data_type == "ndarray":
          if not isinstance(gather_res[0], np.ndarray):
              return np.concatenate([np.asarray(r) for r in gather_res], axis=0)
  
          # order outputs
          return np.concatenate(gather_res, axis=0)
      elif target_data_type == "list":
          out = []
          for r in gather_res:
              out.extend(r)
          return out
      else:
          return gather_res
  
  def set_seed(seed=None):
      random.seed(seed)
      np.random.seed(seed)
      if seed is not None:
          torch.manual_seed(seed)
          torch.cuda.manual_seed_all(seed)
  
  def add_random_background(image, bg_color=None):
      bg_color = np.random.rand() * 255 if bg_color is None else bg_color
      image = np.array(image)
      rgb, alpha = image[..., :3], image[..., 3:]
      alpha = alpha.astype(np.float32) / 255.0
      image_new = rgb * alpha + bg_color * (1 - alpha)
      return Image.fromarray(image_new.astype(np.uint8))