code: 
  
  
  '''simple docstring'''
  
  import unittest
  
  import numpy as np
  
  from transformers import is_tf_available
  from transformers.testing_utils import require_tf
  
  
  if is_tf_available():
   import tensorflow as tf
  
   from transformers.activations_tf import get_tf_activation
  @require_tf
  class 						__lowerCAmelCase		(unittest.TestCase  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        def    UpperCamelCase__	(self					:					List[Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				tf.constant([-100, -1.0, -0.1, 0, 0.1, 1.0, 100.0]				)
               lowercase__       =				get_tf_activation('''gelu'''				)
               lowercase__       =				get_tf_activation('''gelu_10'''				)
  
               lowercase__       =				gelu(UpperCamelCase				)
               lowercase__       =				geluaa(UpperCamelCase				)
  
               lowercase__       =				tf.where(y_gelu_aa < 10.0     ,		1.0     ,		0.0				)
  
               self.assertEqual(tf.math.reduce_max(UpperCamelCase				).numpy().item()     ,		10.0				)
               self.assertTrue(np.allclose(y_gelu * clipped_mask     ,		y_gelu_aa * clipped_mask				)				)
  
  
  
  
  
  
        def    UpperCamelCase__	(self					:					List[str]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               get_tf_activation('''gelu'''				)
               get_tf_activation('''gelu_10'''				)
               get_tf_activation('''gelu_fast'''				)
               get_tf_activation('''gelu_new'''				)
               get_tf_activation('''glu'''				)
               get_tf_activation('''mish'''				)
               get_tf_activation('''quick_gelu'''				)
               get_tf_activation('''relu'''				)
               get_tf_activation('''sigmoid'''				)
               get_tf_activation('''silu'''				)
               get_tf_activation('''swish'''				)
               get_tf_activation('''tanh'''				)
               with self.assertRaises(UpperCamelCase				):
                      get_tf_activation('''bogus'''				)
               with self.assertRaises(UpperCamelCase				):
                      get_tf_activation(UpperCamelCase				)
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
      is_vision_available,
  )
  
  
  lowerCamelCase							:     Optional[Any]								=			{
      'configuration_mobilevitv2': [
          'MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP',
          'MobileViTV2Config',
          'MobileViTV2OnnxConfig',
      ],
  }
  
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   lowerCamelCase							:     Optional[int]								=			[
       'MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST',
       'MobileViTV2ForImageClassification',
       'MobileViTV2ForSemanticSegmentation',
       'MobileViTV2Model',
       'MobileViTV2PreTrainedModel',
   ]
  
  if TYPE_CHECKING:
   from .configuration_mobilevitva import (
       MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
       MobileViTVaConfig,
       MobileViTVaOnnxConfig,
   )
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_mobilevitva import (
        MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
        MobileViTVaForImageClassification,
        MobileViTVaForSemanticSegmentation,
        MobileViTVaModel,
        MobileViTVaPreTrainedModel,
    )
  
  else:
   import sys
  
   lowerCamelCase							:     Dict								=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  