code: 
  
  
  
  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 		_snake_case      (			unittest.TestCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   def lowercase_		(						self					)	->							List[str]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    tf.constant([-1_0_0, -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]:
  
  
  
  
     """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					)
style_context: 
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case = 1_000							)   -> str:
    if (not isinstance(__snake_case						,				__snake_case							)) or (digit_position <= 0):
      raise ValueError('''Digit position must be a positive integer'''							)
    elif (not isinstance(__snake_case						,				__snake_case							)) or (precision < 0):
      raise ValueError('''Precision must be a nonnegative integer'''							)
  
    # compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
    # accurate
    _A           =    (
        4 * _subsum(__snake_case						,				1						,				__snake_case							)
        - 2 * _subsum(__snake_case						,				4						,				__snake_case							)
        - _subsum(__snake_case						,				5						,				__snake_case							)
        - _subsum(__snake_case						,				6						,				__snake_case							)
    )
  
    # return the first hex digit of the fractional part of the result
    return hex(int((sum_result % 1) * 16							)							)[2:]
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case						,				__snake_case							)   -> float:
    _A           =    0.0
    for sum_index in range(digit_pos_to_extract + precision							):
      _A           =    8 * sum_index + denominator_addend
      if sum_index < digit_pos_to_extract:
        # if the exponential term is an integer and we mod it by the denominator
        # before dividing, only the integer part of the sum will change;
        # the fractional part will not
        _A           =    pow(
            16						,				digit_pos_to_extract - 1 - sum_index						,				__snake_case							)
      else:
        _A           =    pow(16						,				digit_pos_to_extract - 1 - sum_index							)
      total += exponential_term / denominator
    return total
  
  
  if __name__ == "__main__":
      import doctest
  
      doctest.testmod()