



"""simple docstring"""
import math

import tensorflow as tf
from packaging import version
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :Any						=			tf.convert_to_tensor(_lowercase	)
						snake_case_ :Optional[int]						=			0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0	),					x.dtype	)	))

						return x * cdf
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :Dict						=			tf.convert_to_tensor(_lowercase	)
						snake_case_ :str						=			tf.cast(math.pi,					x.dtype	)
						snake_case_ :Union[str, Any]						=			tf.cast(0.04_4715,					x.dtype	)
						snake_case_ :Dict						=			0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi	) * (x + coeff * tf.pow(_lowercase,					3	))	))

						return x * cdf
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :str						=			tf.convert_to_tensor(_lowercase	)

						return x * tf.tanh(tf.math.softplus(_lowercase	)	)
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :List[str]						=			tf.convert_to_tensor(_lowercase	)
						snake_case_ :str						=			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_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :str						=			tf.convert_to_tensor(_lowercase	)
						snake_case_ :Optional[Any]						=			tf.cast(1.702,					x.dtype	)
						return x * tf.math.sigmoid(coeff * x	)
def    A_  (			_lowercase	):







						'''simple docstring'''


						return tf.clip_by_value(_gelu(_lowercase	),					-10,					10	)
def    A_  (			_lowercase,					_lowercase=-1	):







						'''simple docstring'''


						snake_case_,       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    A_  (			_lowercase	):







												'''simple docstring'''


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

						__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_  (			_lowercase	):







						'''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()	)}"""	)





"""simple docstring"""
from __future__ import annotations

from collections import Counter
from random import random
class 				lowerCamelCase  :






					'''simple docstring'''





					def __init__(       self:							Tuple						)   ->	Optional[Any]:
											snake_case_ :Optional[int]						=			{}






					def    lowerCAmelCase_				(       self:							Dict						,     snake_case:							str						)   ->	None:
											snake_case_ :str						=			{}






					def    lowerCAmelCase_				(       self:							Optional[int]						,     snake_case:							str						,     snake_case:							str						,     snake_case:							float						)   ->	None:
											if nodea not in self.connections:
																	self.add_node(snake_case						)
											if nodea not in self.connections:
																	self.add_node(snake_case						)
											snake_case_ :Dict						=			probability






					def    lowerCAmelCase_				(       self:							List[Any]						)   ->	list[str]:
											return list(self.connections						)






					def    lowerCAmelCase_				(       self:							Any						,     snake_case:							str						)   ->	str:
											snake_case_ :Optional[Any]						=			0
											snake_case_ :List[str]						=			random()

											for dest in self.connections[node]:
																	current_probability += self.connections[node][dest]
																	if current_probability > random_value:
																							return dest
											return ""

def    A_  (			_lowercase,					_lowercase,					_lowercase	):







						'''simple docstring'''


						snake_case_ :List[str]						=			MarkovChainGraphUndirectedUnweighted()

						for nodea, nodea, probability in transitions:
												graph.add_transition_probability(_lowercase,					_lowercase,					_lowercase	)

						snake_case_ :int						=			Counter(graph.get_nodes()	)
						snake_case_ :Optional[Any]						=			start

						for _ in range(_lowercase	):
												snake_case_ :Tuple						=			graph.transition(_lowercase	)
												visited[node] += 1

						return visited


if __name__ == "__main__":
						import doctest

						doctest.testmod()
