code: 
  
  
  import unittest
  import warnings
  from dataclasses import dataclass
  
  from transformers.convert_slow_tokenizer import SpmConverter
  from transformers.testing_utils import get_tests_dir
  
  
  
  
  
  @dataclass
  class    A					:
  							UpperCamelCase_     :							str
  
  
  
  
  
  class    A					(     unittest.TestCase				):
  
  
  							def     _A (self      ):
  									__lowercase=    get_tests_dir('fixtures/test_sentencepiece.model'      )
  									__lowercase=    get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model'      )
  
  									__lowercase=    FakeOriginalTokenizer(vocab_file=lowerCAmelCase      )
  
  									with warnings.catch_warnings(record=lowerCAmelCase      ) as w:
  											__lowercase=    SpmConverter(lowerCAmelCase      )
  									self.assertEqual(len(lowerCAmelCase      )   ,       0      )
  
  									__lowercase=    FakeOriginalTokenizer(vocab_file=lowerCAmelCase      )
  
  									with warnings.catch_warnings(record=lowerCAmelCase      ) as w:
  											__lowercase=    SpmConverter(lowerCAmelCase      )
  									self.assertEqual(len(lowerCAmelCase      )   ,       1      )
  
  									self.assertIn(
  									    'The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option'
  									    ' which is not implemented in the fast tokenizers.'   ,       str(w[0].message      )   ,       )
  
  
  
  
  
  
  
style_context: 
  
  
  from dataclasses import dataclass
  from enum import Enum
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  from PIL import Image
  
  from ...utils import BaseOutput, is_torch_available, is_transformers_available
  
  
  
  
  
  @dataclass
  class    A					(     A_				):
  							UpperCamelCase_     :							Optional[int] ={
  							    '''sld_warmup_steps''': 15,
  							    '''sld_guidance_scale''': 20,
  							    '''sld_threshold''': 0.0,
  							    '''sld_momentum_scale''': 0.0,
  							    '''sld_mom_beta''': 0.0,
  							}
  							UpperCamelCase_     :							Tuple ={
  							    '''sld_warmup_steps''': 10,
  							    '''sld_guidance_scale''': 1_000,
  							    '''sld_threshold''': 0.01,
  							    '''sld_momentum_scale''': 0.3,
  							    '''sld_mom_beta''': 0.4,
  							}
  							UpperCamelCase_     :							Union[str, Any] ={
  							    '''sld_warmup_steps''': 7,
  							    '''sld_guidance_scale''': 2_000,
  							    '''sld_threshold''': 0.025,
  							    '''sld_momentum_scale''': 0.5,
  							    '''sld_mom_beta''': 0.7,
  							}
  							UpperCamelCase_     :							List[Any] ={
  							    '''sld_warmup_steps''': 0,
  							    '''sld_guidance_scale''': 5_000,
  							    '''sld_threshold''': 1.0,
  							    '''sld_momentum_scale''': 0.5,
  							    '''sld_mom_beta''': 0.7,
  							}
  
  
  
  
  
  @dataclass
  class    A					(     A_				):
  							UpperCamelCase_     :							Union[List[PIL.Image.Image], np.ndarray]
  							UpperCamelCase_     :							Optional[List[bool]]
  							UpperCamelCase_     :							Optional[Union[List[PIL.Image.Image], np.ndarray]]
  							UpperCamelCase_     :							Optional[str]
  
  
  if is_transformers_available() and is_torch_available():
  	from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
  	from .safety_checker import SafeStableDiffusionSafetyChecker
  
  
  
  
  
  
  