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			):
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						int								=				tf.constant([-100, -1.0, -0.1, 0, 0.1, 1.0, 100.0]							)
  					snake_case		:						Union[str, Any]								=				get_tf_activation("gelu"							)
  					snake_case		:						Optional[int]								=				get_tf_activation("gelu_10"							)
  
  					snake_case		:						Optional[int]								=				gelu(SCREAMING_SNAKE_CASE							)
  					snake_case		:						int								=				geluaa(SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						str								=				tf.where(y_gelu_aa < 10.0				, 1.0				, 0.0							)
  
  					self.assertEqual(tf.math.reduce_max(SCREAMING_SNAKE_CASE							).numpy().item()				, 10.0							)
  					self.assertTrue(np.allclose(y_gelu * clipped_mask				, y_gelu_aa * clipped_mask							)							)
  
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""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(SCREAMING_SNAKE_CASE							):
  							get_tf_activation("bogus"							)
  					with self.assertRaises(SCREAMING_SNAKE_CASE							):
  							get_tf_activation(SCREAMING_SNAKE_CASE							)
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  from ...processing_utils import ProcessorMixin
  from ...tokenization_utils_base import BatchEncoding
  class 			lowerCamelCase__    (  lowerCamelCase_			):
  			a__ :      Tuple								=     ["""image_processor""", """tokenizer"""]
  			a__ :      List[Any]								=     """EfficientNetImageProcessor"""
  			a__ :      str								=     ("""BertTokenizer""", """BertTokenizerFast""")
  
  
  			def __init__(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					super().__init__(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)
  
  
  			def __call__(     self				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE="max_length"				, SCREAMING_SNAKE_CASE=64				, SCREAMING_SNAKE_CASE=None				, **SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					if text is None and images is None:
  							raise ValueError("You have to specify either text or images. Both cannot be none."							)
  
  					if text is not None:
  							snake_case		:						Dict								=				self.tokenizer(
  							    SCREAMING_SNAKE_CASE				, padding=SCREAMING_SNAKE_CASE				, max_length=SCREAMING_SNAKE_CASE				, return_tensors=SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)
  
  					if images is not None:
  							snake_case		:						Tuple								=				self.image_processor(SCREAMING_SNAKE_CASE				, return_tensors=SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)
  
  					if text is not None and images is not None:
  							snake_case		:						Dict								=				image_features.pixel_values
  							return encoding
  					elif text is not None:
  							return encoding
  					else:
  							return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE							)				, tensor_type=SCREAMING_SNAKE_CASE							)
  
  
  			def 							lowerCamelCase_				(     self				, *SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)
  
  
  			def 							lowerCamelCase_				(     self				, *SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return self.tokenizer.decode(*SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)
  
  
  
  			@property
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[Any]								=				self.tokenizer.model_input_names
  					snake_case		:						Any								=				self.image_processor.model_input_names
  					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)
  
  