
'''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 			UpperCamelCase__	(     unittest.TestCase):






 def 						lowercase_    (      self	:Dict      )					->      Any:







       '''simple docstring'''



       __A													=      tf.constant([-100, -1.0, -0.1, 0, 0.1, 1.0, 100.0]      )
       __A													=      get_tf_activation('gelu'      )
       __A													=      get_tf_activation('gelu_10'      )

       __A													=      gelu(_A      )
       __A													=      geluaa(_A      )

       __A													=      tf.where(y_gelu_aa < 10.0						,					1.0						,					0.0      )

       self.assertEqual(tf.math.reduce_max(_A      ).numpy().item()						,					10.0      )
       self.assertTrue(np.allclose(y_gelu * clipped_mask						,					y_gelu_aa * clipped_mask      )      )






 def 						lowercase_    (      self	:Union[str, Any]      )					->      int:







       '''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(_A      ):
             get_tf_activation('bogus'      )
       with self.assertRaises(_A      ):
             get_tf_activation(_A      )








'''simple docstring'''





from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
 if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.27.0")):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_torch_and_transformers_objects import (
     AudioLDMPipeline,
 )
else:
 from .pipeline_audioldm import AudioLDMPipeline






