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 			A							(       unittest.TestCase				):
  
  
  
  			def A__ (					self       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       tf.constant([-100, -1.0, -0.1, 0, 0.1, 1.0, 1_00.0]       )
  									lowercase__							 =       get_tf_activation("""gelu"""       )
  									lowercase__							 =       get_tf_activation("""gelu_10"""       )
  
  									lowercase__							 =       gelu(__snake_case       )
  									lowercase__							 =       geluaa(__snake_case       )
  
  									lowercase__							 =       tf.where(y_gelu_aa < 10.0       ,     1.0       ,     0.0       )
  
  									self.assertEqual(tf.math.reduce_max(__snake_case       ).numpy().item()       ,     10.0       )
  									self.assertTrue(np.allclose(y_gelu * clipped_mask       ,     y_gelu_aa * clipped_mask       )       )
  
  
  
  			def A__ (					self       )			->		Dict:
  
  
  
  
  									'''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(__snake_case       ):
  															get_tf_activation("""bogus"""       )
  									with self.assertRaises(__snake_case       ):
  															get_tf_activation(__snake_case       )
  
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  __A          =    {"configuration_detr": ["DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetrConfig", "DetrOnnxConfig"]}
  
  try:
  				if not is_vision_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				__A          =    ["DetrFeatureExtractor"]
  				__A          =    ["DetrImageProcessor"]
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				__A          =    [
  				    "DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
  				    "DetrForObjectDetection",
  				    "DetrForSegmentation",
  				    "DetrModel",
  				    "DetrPreTrainedModel",
  				]
  
  
  if TYPE_CHECKING:
  				from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig
  
  				try:
  								if not is_vision_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .feature_extraction_detr import DetrFeatureExtractor
  								from .image_processing_detr import DetrImageProcessor
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_detr import (
  								    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    DetrForObjectDetection,
  								    DetrForSegmentation,
  								    DetrModel,
  								    DetrPreTrainedModel,
  								)
  
  else:
  				import sys
  
  				__A          =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
  