






import math

import tensorflow as tf
from packaging import version





def 	SCREAMING_SNAKE_CASE__    ( snake_case_	) ->	Dict:


	"""simple docstring"""



	a												=      tf.convert_to_tensor(lowerCamelCase__	)
	a												=      0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0	),	x.dtype	)	))

	return x * cdf





def 	SCREAMING_SNAKE_CASE__    ( snake_case_	) ->	int:


	"""simple docstring"""



	a												=      tf.convert_to_tensor(lowerCamelCase__	)
	a												=      tf.cast(math.pi,	x.dtype	)
	a												=      tf.cast(0.04_4715,	x.dtype	)
	a												=      0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi	) * (x + coeff * tf.pow(lowerCamelCase__,	3	))	))

	return x * cdf





def 	SCREAMING_SNAKE_CASE__    ( snake_case_	) ->	Optional[int]:


	"""simple docstring"""



	a												=      tf.convert_to_tensor(lowerCamelCase__	)

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





def 	SCREAMING_SNAKE_CASE__    ( snake_case_	) ->	Optional[Any]:


	"""simple docstring"""



	a												=      tf.convert_to_tensor(lowerCamelCase__	)
	a												=      tf.cast(0.04_4715,	x.dtype	)
	a												=      tf.cast(0.79_7884_5608,	x.dtype	)

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





def 	SCREAMING_SNAKE_CASE__    ( snake_case_	) ->	int:


	"""simple docstring"""



	a												=      tf.convert_to_tensor(lowerCamelCase__	)
	a												=      tf.cast(1.702,	x.dtype	)
	return x * tf.math.sigmoid(coeff * x	)





def 	SCREAMING_SNAKE_CASE__    ( snake_case_	) ->	str:


	"""simple docstring"""



	return tf.clip_by_value(_gelu(lowerCamelCase__	),	-1_0,	1_0	)





def 	SCREAMING_SNAKE_CASE__    ( snake_case_,	snake_case_=-1	) ->	Union[str, Any]:


	"""simple docstring"""



	a						,			a												=      tf.split(lowerCamelCase__,	2,	axis=lowerCamelCase__	)
	return a * tf.math.sigmoid(lowerCamelCase__	)


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





				def 	SCREAMING_SNAKE_CASE__    ( snake_case_	) ->	Dict:


					"""simple docstring"""



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

				UpperCamelCase__    :   Union[str, Any]    		=       tf.keras.activations.gelu
				UpperCamelCase__    :   int    		=       approximate_gelu_wrap
else:
				UpperCamelCase__    :   Dict    		=       _gelu
				UpperCamelCase__    :   Dict    		=       _gelu_new


UpperCamelCase__    :   List[str]    		=       {
    "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 	SCREAMING_SNAKE_CASE__    ( snake_case_	) ->	Tuple:


	"""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()	)}"""	)









import random
from typing import Any






def 	__lowerCamelCase						(							lowerCamelCase__		:						list      ):





			'''simple docstring'''




			for _ in range(len(lowerCamelCase__      )      ):
						lowerCamelCase    		=  random.randint(0  ,							len(lowerCamelCase__      ) - 1      )
						lowerCamelCase    		=  random.randint(0  ,							len(lowerCamelCase__      ) - 1      )
						lowerCamelCase		,		lowerCamelCase    		=  data[b], data[a]
			return data


if __name__ == "__main__":
		UpperCAmelCase   :    str     =		[0, 1, 2, 3, 4, 5, 6, 7]
		UpperCAmelCase   :    Union[str, Any]     =		["python", "says", "hello", "!"]
		print("Fisher-Yates Shuffle:")
		print("List", integers, strings)
		print("FY Shuffle", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))

