
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 						snake_case_     ( self						:    Any			):
          __lowercase						:     List[str]		      =    tf.constant([-100, -1.0, -0.1, 0, 0.1, 1.0, 1_00.0]			)
          __lowercase						:     List[str]		      =    get_tf_activation('''gelu'''			)
          __lowercase						:     Any		      =    get_tf_activation('''gelu_10'''			)

          __lowercase						:     Optional[Any]		      =    gelu(_snake_case			)
          __lowercase						:     List[str]		      =    geluaa(_snake_case			)

          __lowercase						:     Dict		      =    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 						snake_case_     ( self						:    Union[str, Any]			):
          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			)




__lowerCAmelCase   :     List[Any]      =						"\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

__lowerCAmelCase   :     Optional[Any]      =						[{"type": "code", "content": INSTALL_CONTENT}]
__lowerCAmelCase   :     List[str]      =						{
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}


