code: def test_check_encoding_slow_fast(self):
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
          with self.subTest(
              f"{tokenizer.__class__.__name__} ({pretrained_name})"
          ):
              tokenizer_s = self.tokenizer_class.from_pretrained(
                  pretrained_name, **kwargs
              )
              tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                  pretrained_name, **kwargs
              )
  
              text = "A\n'll 11p223RF☆ho!!to?'d'd''d of a cat"
              text_tokenized_s = tokenizer_s.tokenize(text)
              text_tokenized_r = tokenizer_r.tokenize(text)
  
              self.assertListEqual(text_tokenized_s, text_tokenized_r)
  
              # Test that the tokenization is identical on an example containing a character (Latin Small Letter A
              # with Tilde) encoded in 2 different ways
              text = "xa\u0303y" + " " + "x\xe3y"
              text_tokenized_s = tokenizer_s.tokenize(text)
              text_tokenized_r = tokenizer_r.tokenize(text)
  
              self.assertListEqual(text_tokenized_s, text_tokenized_r)
  
              # Test that the tokenization is identical on unicode of space type
              spaces_unicodes = [
                  "\u0009",  # (horizontal tab, '\t')
                  "\u000B",  # (vertical tab)
                  "\u000C",  # (form feed)
                  "\u0020",  # (space, ' ')
                  "\u200E",  # (left-to-right mark):w
                  "\u200F",  # (right-to-left mark)
              ]
              for unicode_seq in spaces_unicodes:
                  text_tokenized_s = tokenizer_s.tokenize(unicode_seq)
                  text_tokenized_r = tokenizer_r.tokenize(unicode_seq)
  
                  self.assertListEqual(
                      text_tokenized_s, text_tokenized_r
                  )
  
              # Test that the tokenization is identical on unicode of line break type
              line_break_unicodes = [
                  "\u000A",  # (line feed, '\n')
                  "\r\n",  # (carriage return and line feed, '\r\n')
                  "\u000D",  # (carriage return, '\r')
                  "\r",  # (carriage return, '\r')
                  "\u000D",  # (carriage return, '\r')
                  "\u2028",  # (line separator)
                  "\u2029",  # (paragraph separator)
                  # "\u0085", # (next line)
              ]
  
              # The tokenization is not identical for the character "\u0085" (next line). The slow version transforms
              # it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
              # space (and thus into an empty list).
  
              for unicode_seq in line_break_unicodes:
                  text_tokenized_s = tokenizer_s.tokenize(unicode_seq)
                  text_tokenized_r = tokenizer_r.tokenize(unicode_seq)
  
                  self.assertListEqual(
                      text_tokenized_s, text_tokenized_r
                  )
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/tests/models/clip/test_tokenization_clip.py
imports: import json
  import os
  import unittest
  from transformers import CLIPTokenizer, CLIPTokenizerFast
  from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
  from transformers.testing_utils import require_ftfy, require_tokenizers
  from ...test_tokenization_common import TokenizerTesterMixin
label_desc: Use image node_id 6 for calling the CLIPTokenizationTest obj's underlying member method code with example usage: obj.test_check_encoding_slow_fast() without return types