


import math

import tensorflow as tf
from packaging import version


def 			_UpperCamelCase				( lowercase__ ):
						__SCREAMING_SNAKE_CASE		:	str						     =						tf.convert_to_tensor(lowercase__ )
						__SCREAMING_SNAKE_CASE		:	List[str]						     =						0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 )       ,			x.dtype ) ))

						return x * cdf


def 			_UpperCamelCase				( lowercase__ ):
						__SCREAMING_SNAKE_CASE		:	str						     =						tf.convert_to_tensor(lowercase__ )
						__SCREAMING_SNAKE_CASE		:	Optional[Any]						     =						tf.cast(math.pi       ,			x.dtype )
						__SCREAMING_SNAKE_CASE		:	int						     =						tf.cast(0.04_4715       ,			x.dtype )
						__SCREAMING_SNAKE_CASE		:	Tuple						     =						0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi ) * (x + coeff * tf.pow(lowercase__       ,			3 )) ))

						return x * cdf


def 			_UpperCamelCase				( lowercase__ ):
						__SCREAMING_SNAKE_CASE		:	Any						     =						tf.convert_to_tensor(lowercase__ )

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


def 			_UpperCamelCase				( lowercase__ ):
						__SCREAMING_SNAKE_CASE		:	Optional[Any]						     =						tf.convert_to_tensor(lowercase__ )
						__SCREAMING_SNAKE_CASE		:	Union[str, Any]						     =						tf.cast(0.04_4715       ,			x.dtype )
						__SCREAMING_SNAKE_CASE		:	Tuple						     =						tf.cast(0.79_7884_5608       ,			x.dtype )

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


def 			_UpperCamelCase				( lowercase__ ):
						__SCREAMING_SNAKE_CASE		:	List[str]						     =						tf.convert_to_tensor(lowercase__ )
						__SCREAMING_SNAKE_CASE		:	Dict						     =						tf.cast(1.702       ,			x.dtype )
						return x * tf.math.sigmoid(coeff * x )


def 			_UpperCamelCase				( lowercase__ ):
						return tf.clip_by_value(_gelu(lowercase__ )       ,			-10       ,			10 )


def 			_UpperCamelCase				( lowercase__       ,			lowercase__=-1 ):
						__SCREAMING_SNAKE_CASE		:	List[Any]						     =						tf.split(lowercase__       ,			2       ,			axis=lowercase__ )
						return a * tf.math.sigmoid(lowercase__ )


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


						def 			_UpperCamelCase				( lowercase__ ):
												return tf.keras.activations.gelu(lowercase__       ,			approximate=lowercase__ )

						__lowerCAmelCase      :					List[str]    							=tf.keras.activations.gelu
						__lowerCAmelCase      :					Dict    							=approximate_gelu_wrap
else:
						__lowerCAmelCase      :					Dict    							=_gelu
						__lowerCAmelCase      :					str    							=_gelu_new


__lowerCAmelCase      :					Any    							={
    "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 			_UpperCamelCase				( lowercase__ ):
						if activation_string in ACTaFN:
												return ACTaFN[activation_string]
						else:
												raise KeyError(F'''function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys() )}''' )






from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean




def 		lowerCAmelCase_ (						lowerCamelCase ):

				# A local function to see if a dot lands in the circle.
				def is_in_circle(lowerCamelCase						,  lowerCamelCase ) -> bool:
								__magic_name__						:						Dict										=sqrt((x**2) + (y**2) )
								# Our circle has a radius of 1, so a distance
								# greater than 1 would land outside the circle.
								return distance_from_centre <= 1

				# The proportion of guesses that landed in the circle
				__magic_name__						:						Union[str, Any]										=mean(
				    int(is_in_circle(uniform(-1.0						,  1.0 )						,  uniform(-1.0						,  1.0 ) ) )
				    for _ in range(lowerCamelCase ) )
				# The ratio of the area for circle to square is pi/4.
				__magic_name__						:						List[Any]										=proportion * 4
				print(F"The estimated value of pi is {pi_estimate}" )
				print(F"The numpy value of pi is {pi}" )
				print(F"The total error is {abs(pi - pi_estimate )}" )




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase = 0.0						,  lowerCamelCase = 1.0						,  ):
				return mean(
				    function_to_integrate(uniform(lowerCamelCase						,  lowerCamelCase ) ) for _ in range(lowerCamelCase ) ) * (max_value - min_value)




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase = 0.0						,  lowerCamelCase = 1.0 ):

				def identity_function(lowerCamelCase ) -> float:
								return x

				__magic_name__						:						Optional[int]										=area_under_curve_estimator(
				    lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase )
				__magic_name__						:						str										=(max_value * max_value - min_value * min_value) / 2

				print("""******************""" )
				print(F"Estimating area under y=x where x varies from {min_value} to {max_value}" )
				print(F"Estimated value is {estimated_value}" )
				print(F"Expected value is {expected_value}" )
				print(F"Total error is {abs(estimated_value - expected_value )}" )
				print("""******************""" )




def 		lowerCAmelCase_ (						lowerCamelCase ):

				def function_to_integrate(lowerCamelCase ) -> float:
								return sqrt(4.0 - x * x )

				__magic_name__						:						Dict										=area_under_curve_estimator(
				    lowerCamelCase						,  lowerCamelCase						,  0.0						,  2.0 )

				print("""******************""" )
				print("""Estimating pi using area_under_curve_estimator""" )
				print(F"Estimated value is {estimated_value}" )
				print(F"Expected value is {pi}" )
				print(F"Total error is {abs(estimated_value - pi )}" )
				print("""******************""" )


if __name__ == "__main__":
							import doctest

							doctest.testmod()




