


'''simple docstring'''





from __future__ import annotations

UpperCAmelCase__  			=				[-1_0, -5, 0, 5, 5.1, 1_1, 1_3, 2_1, 3, 4, -2_1, -1_0, -5, -1, 0]
UpperCAmelCase__  			=				[-5, 0, 5, 5.1, 1_1, 1_3, 2_1, -1, 4, -1, -1_0, -5, -1, 0, -1]






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int ):






			"""simple docstring"""




			__A=	[]
			__A=	len(__UpperCAmelCase )

			for i in range(__UpperCAmelCase ):
						__A=	-1
						for j in range(i + 1,__UpperCAmelCase ):
									if arr[i] < arr[j]:
												__A=	arr[j]
												break
						result.append(__UpperCAmelCase )
			return result






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Tuple ):






			"""simple docstring"""




			__A=	[]
			for i, outer in enumerate(__UpperCAmelCase ):
						__A=	-1
						for inner in arr[i + 1 :]:
									if outer < inner:
												__A=	inner
												break
						result.append(__UpperCAmelCase )
			return result






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Optional[Any] ):






			"""simple docstring"""




			__A=	len(__UpperCAmelCase )
			__A=	[]
			__A=	[-1] * arr_size

			for index in reversed(range(__UpperCAmelCase ) ):
						if stack:
									while stack[-1] <= arr[index]:
												stack.pop()
												if not stack:
															break
						if stack:
									__A=	stack[-1]
						stack.append(arr[index] )
			return result


if __name__ == "__main__":
					from doctest import testmod
					from timeit import timeit

					testmod()
					print(next_greatest_element_slow(arr))
					print(next_greatest_element_fast(arr))
					print(next_greatest_element(arr))

					UpperCAmelCase__  			=				(
					    '''from __main__ import arr, next_greatest_element_slow, '''
					    '''next_greatest_element_fast, next_greatest_element'''
					)
					print(
					    '''next_greatest_element_slow():''',
					    timeit('''next_greatest_element_slow(arr)''', setup=setup),
					)
					print(
					    '''next_greatest_element_fast():''',
					    timeit('''next_greatest_element_fast(arr)''', setup=setup),
					)
					print(
					    '''     next_greatest_element():''',
					    timeit('''next_greatest_element(arr)''', setup=setup),
					)








'''simple docstring'''

import math

import tensorflow as tf
from packaging import version







def a_     (    __UpperCAmelCase					)				->					Optional[Any]:





			"""simple docstring"""







			snake_case:    str      							=tf.convert_to_tensor(__UpperCAmelCase					)
			snake_case:    int      							=0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0					)				,							x.dtype					)					))

			return x * cdf







def a_     (    __UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			snake_case:    Optional[int]      							=tf.convert_to_tensor(__UpperCAmelCase					)
			snake_case:    Tuple      							=tf.cast(math.pi				,							x.dtype					)
			snake_case:    Dict      							=tf.cast(0.04_4715				,							x.dtype					)
			snake_case:    List[str]      							=0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi					) * (x + coeff * tf.pow(__UpperCAmelCase				,							3					))					))

			return x * cdf







def a_     (    __UpperCAmelCase					)				->					Tuple:





			"""simple docstring"""







			snake_case:    Optional[int]      							=tf.convert_to_tensor(__UpperCAmelCase					)

			return x * tf.tanh(tf.math.softplus(__UpperCAmelCase					)					)







def a_     (    __UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			snake_case:    List[str]      							=tf.convert_to_tensor(__UpperCAmelCase					)
			snake_case:    Dict      							=tf.cast(0.04_4715				,							x.dtype					)
			snake_case:    List[str]      							=tf.cast(0.79_7884_5608				,							x.dtype					)

			return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)					))







def a_     (    __UpperCAmelCase					)				->					Optional[Any]:





			"""simple docstring"""







			snake_case:    Union[str, Any]      							=tf.convert_to_tensor(__UpperCAmelCase					)
			snake_case:    Tuple      							=tf.cast(1.702				,							x.dtype					)
			return x * tf.math.sigmoid(coeff * x					)







def a_     (    __UpperCAmelCase					)				->					int:





			"""simple docstring"""







			return tf.clip_by_value(_gelu(__UpperCAmelCase					)				,							-10				,							10					)







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase=-1					)				->					List[str]:





			"""simple docstring"""







			snake_case							,  snake_case:    int      							=tf.split(__UpperCAmelCase				,							2				,							axis=__UpperCAmelCase					)
			return a * tf.math.sigmoid(__UpperCAmelCase					)


if version.parse(tf.version.VERSION) >= version.parse('2.4'):







	def a_     (    __UpperCAmelCase					)				->					int:





				"""simple docstring"""







				return tf.keras.activations.gelu(__UpperCAmelCase				,							approximate=__UpperCAmelCase					)

	a							   =				tf.keras.activations.gelu







	a							   =				approximate_gelu_wrap
else:
	a							   =				_gelu
	a							   =				_gelu_new


a							   =				{
    'gelu': gelu,
    'gelu_10': gelu_aa,
    'gelu_fast': gelu_fast,
    'gelu_new': gelu_new,
    'glu': glu,
    'mish': mish,
    'quick_gelu': quick_gelu,
    'relu': tf.keras.activations.relu,
    'sigmoid': tf.keras.activations.sigmoid,
    'silu': tf.keras.activations.swish,
    'swish': tf.keras.activations.swish,
    'tanh': tf.keras.activations.tanh,
}







def a_     (    __UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			if activation_string in ACTaFN:
						return ACTaFN[activation_string]
			else:
						raise KeyError(f'''function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()					)}'''					)




