content: import hashlib
  import os
  import urllib
  import warnings
  from typing import Any, Union, List
  from pkg_resources import packaging
  
  import torch
  from PIL import Image
  from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
  from tqdm import tqdm
  
  from .model import build_model
  from .simple_tokenizer import SimpleTokenizer as _Tokenizer
  
  try:
      from torchvision.transforms import InterpolationMode
      BICUBIC = InterpolationMode.BICUBIC
  except ImportError:
      BICUBIC = Image.BICUBIC
  
  
  if packaging.version.parse(torch.__version__) < packaging.version.parse("1.7.1"):
      warnings.warn("PyTorch version 1.7.1 or higher is recommended")
  
  
  __all__ = ["available_models", "load", "tokenize"]
  _tokenizer = _Tokenizer()
  
  _MODELS = {
      "ViT-B/32": "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt",
      "ViT-B/16": "https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt",
      "ViT-L/14": "https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt",
  }
  
  
  def _download(url: str, root: str):
      os.makedirs(root, exist_ok=True)
      filename = os.path.basename(url)
  
      expected_sha256 = url.split("/")[-2]
      download_target = os.path.join(root, filename)
  
      if os.path.exists(download_target) and not os.path.isfile(download_target):
          raise RuntimeError(f"{download_target} exists and is not a regular file")
  
      if os.path.isfile(download_target):
          if hashlib.sha256(open(download_target, "rb").read()).hexdigest() == expected_sha256:
              return download_target
          else:
              warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file")
  
      with urllib.request.urlopen(url) as source, open(download_target, "wb") as output:
          with tqdm(total=int(source.info().get("Content-Length")), ncols=80, unit='iB', unit_scale=True, unit_divisor=1024) as loop:
              while True:
                  buffer = source.read(8192)
                  if not buffer:
                      break
  
                  output.write(buffer)
                  loop.update(len(buffer))
  
      if hashlib.sha256(open(download_target, "rb").read()).hexdigest() != expected_sha256:
          raise RuntimeError(f"Model has been downloaded but the SHA256 checksum does not not match")
  
      return download_target
  
  
  def _convert_image_to_rgb(image):
      return image.convert("RGB")
  
  
  def _transform(n_px):
      return Compose([
          Resize(n_px, interpolation=BICUBIC),
          CenterCrop(n_px),
          _convert_image_to_rgb,
          ToTensor(),
          Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
      ])
  
  
  def available_models() -> List[str]:
      """Returns the names of available CLIP models"""
      return list(_MODELS.keys())
  
  
  def load(
      name: str, 
      device: Union[str, torch.device] = "cuda" if torch.cuda.is_available() else "cpu", 
      jit: bool = False, download_root: str = None,
      # evl
      n_layers=12, n_dim=768, n_head=12, mlp_factor=4.0, drop_path_rate=0.,
      mlp_dropout=[0.0, 0.0, 0.0, 0.0], cls_dropout=0.5, t_size=8, spatial_size=14,
      use_t_conv=True, use_image_attnmap=True, use_t_pos_embed=True, dropout=0.0, no_pretrain=False, init_zero=True, mergeclip=False, mergeweight=0.5, use_capdecoder=False, clip_state_dict=None,
      ):
      """Load a CLIP model
  
      Parameters
      ----------
      name : str
          A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict
  
      device : Union[str, torch.device]
          The device to put the loaded model
  
      jit : bool
          Whether to load the optimized JIT model or more hackable non-JIT model (default).
  
      download_root: str
          path to download the model files; by default, it uses "~/.cache/clip"
  
      Returns
      -------
      model : torch.nn.Module
          The CLIP model
  
      preprocess : Callable[[PIL.Image], torch.Tensor]
          A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input
      """
      if name in _MODELS:
          model_path = _download(_MODELS[name], download_root or os.path.expanduser("~/.cache/clip"))
      elif os.path.isfile(name):
          model_path = name
      else:
          raise RuntimeError(f"Model {name} not found; available models = {available_models()}")
      
      '''
      with open(model_path, 'rb') as opened_file:
          try:
              # loading JIT archive
              model = torch.jit.load(opened_file, map_location=device if jit else "cpu").eval()
              state_dict = None
          except RuntimeError:
              # loading saved state dict
              if jit:
                  warnings.warn(f"File {model_path} is not a JIT archive. Loading as a state dict instead")
                  jit = False
              state_dict = torch.load(model_path, map_location="cpu")
      ''' 
      init_state_dict = torch.load(model_path, map_location='cpu')['state_dict']
      state_dict = {}
      for k, v in init_state_dict.items():
          k = k.replace('clip.','')
          state_dict[k] = v
  
      if not jit:
          model = build_model(
              state_dict or model.state_dict(),
              n_layers=n_layers, n_dim=n_dim, n_head=n_head, mlp_factor=mlp_factor, drop_path_rate=drop_path_rate,
              mlp_dropout=mlp_dropout, cls_dropout=cls_dropout, t_size=t_size, spatial_size=spatial_size,
              use_t_conv=use_t_conv, use_image_attnmap=use_image_attnmap, use_t_pos_embed=use_t_pos_embed, no_pretrain=no_pretrain,
              init_zero=init_zero, mergeclip=mergeclip, mergeweight=mergeweight, use_capdecoder=use_capdecoder, clip_state_dict=clip_state_dict,
          ).to(device)
          if str(device) == "cpu":
              model.float()
          return model, _transform(model.visual.input_resolution)
  
      # patch the device names
      device_holder = torch.jit.trace(lambda: torch.ones([]).to(torch.device(device)), example_inputs=[])
      device_node = [n for n in device_holder.graph.findAllNodes("prim::Constant") if "Device" in repr(n)][-1]
  
      def patch_device(module):
          try:
              graphs = [module.graph] if hasattr(module, "graph") else []
          except RuntimeError:
              graphs = []
  
          if hasattr(module, "forward1"):
              graphs.append(module.forward1.graph)
  
          for graph in graphs:
              for node in graph.findAllNodes("prim::Constant"):
                  if "value" in node.attributeNames() and str(node["value"]).startswith("cuda"):
                      node.copyAttributes(device_node)
  
      model.apply(patch_device)
      patch_device(model.encode_image)
      patch_device(model.encode_text)
  
      # patch dtype to float32 on CPU
      if str(device) == "cpu":
          float_holder = torch.jit.trace(lambda: torch.ones([]).float(), example_inputs=[])
          float_input = list(float_holder.graph.findNode("aten::to").inputs())[1]
          float_node = float_input.node()
  
          def patch_float(module):
              try:
                  graphs = [module.graph] if hasattr(module, "graph") else []
              except RuntimeError:
                  graphs = []
  
              if hasattr(module, "forward1"):
                  graphs.append(module.forward1.graph)
  
              for graph in graphs:
                  for node in graph.findAllNodes("aten::to"):
                      inputs = list(node.inputs())
                      for i in [1, 2]:  # dtype can be the second or third argument to aten::to()
                          if inputs[i].node()["value"] == 5:
                              inputs[i].node().copyAttributes(float_node)
  
          model.apply(patch_float)
          patch_float(model.encode_image)
          patch_float(model.encode_text)
  
          model.float()
  
      return model, _transform(model.input_resolution.item())
  
  
  def tokenize(texts: Union[str, List[str]], context_length: int = 77, truncate: bool = False, return_special_tokens_mask: bool = False) -> Union[torch.IntTensor, torch.LongTensor, torch.BoolTensor]:
      """
      Returns the tokenized representation of given input string(s)
  
      Parameters
      ----------
      texts : Union[str, List[str]]
          An input string or a list of input strings to tokenize
  
      context_length : int
          The context length to use; all CLIP models use 77 as the context length
  
      truncate: bool
          Whether to truncate the text in case its encoding is longer than the context length
  
      Returns
      -------
      A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length].
      We return LongTensor when torch version is <1.8.0, since older index_select requires indices to be long.
      """
      if isinstance(texts, str):
          texts = [texts]
  
      sot_token = _tokenizer.encoder["<|startoftext|>"]
      eot_token = _tokenizer.encoder["<|endoftext|>"]
      all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts]
      if packaging.version.parse(torch.__version__) < packaging.version.parse("1.8.0"):
          result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)
      else:
          result = torch.zeros(len(all_tokens), context_length, dtype=torch.int)
      
      special_tokens_mask = torch.zeros(len(all_tokens), context_length, dtype=torch.bool)
  
      for i, tokens in enumerate(all_tokens):
          if len(tokens) > context_length:
              if truncate:
                  tokens = tokens[:context_length]
                  tokens[-1] = eot_token
              else:
                  raise RuntimeError(f"Input {texts[i]} is too long for context length {context_length}")
          result[i, :len(tokens)] = torch.tensor(tokens)
          special_tokens_mask[i, len(tokens):] = 1
      
      if return_special_tokens_mask:
          return result, special_tokens_mask
  
      return result
  