code: def setUp(self):
      self.tmpdirname = tempfile.mkdtemp()
  
      vocab = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]  # fmt: skip
      vocab_tokens = dict(zip(vocab, range(len(vocab))))
      merges = ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
      self.special_tokens_map = {"unk_token": "<unk>"}
  
      self.vocab_file = os.path.join(
          self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]
      )
      self.merges_file = os.path.join(
          self.tmpdirname, VOCAB_FILES_NAMES["merges_file"]
      )
      with open(self.vocab_file, "w", encoding="utf-8") as fp:
          fp.write(json.dumps(vocab_tokens) + "\n")
      with open(self.merges_file, "w", encoding="utf-8") as fp:
          fp.write("\n".join(merges))
  
      image_processor_map = {
          "do_resize": True,
          "size": 20,
          "do_center_crop": True,
          "crop_size": 18,
          "do_normalize": True,
          "image_mean": [0.48145466, 0.4578275, 0.40821073],
          "image_std": [0.26862954, 0.26130258, 0.27577711],
      }
      self.image_processor_file = os.path.join(
          self.tmpdirname, IMAGE_PROCESSOR_NAME
      )
      with open(self.image_processor_file, "w", encoding="utf-8") as fp:
          json.dump(image_processor_map, fp)
  
imports: import json
  import os
  import shutil
  import tempfile
  import unittest
  import numpy
  import pytest
  from transformers import CLIPTokenizer, CLIPTokenizerFast
  from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
  from transformers.testing_utils import require_vision
  from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
label_desc: Use image node_id 1 for calling the CLIPProcessorTest obj's underlying member method code with example usage: obj.setUp() without return types