
'''simple docstring'''





import math

import tensorflow as tf
from packaging import version

def      A (       A_							:					str       ):
						snake_case       :     str =    tf.convert_to_tensor(A_       )
						snake_case       :     Dict =    0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0       )    ,							x.dtype       )       ))

						return x * cdf

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

						return x * cdf

def      A (       A_							:					Union[str, Any]       ):
						snake_case       :     Union[str, Any] =    tf.convert_to_tensor(A_       )

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

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

def      A (       A_							:					int       ):
						return tf.clip_by_value(_gelu(A_       )    ,							-10    ,							10       )

def      A (       A_							:					Optional[int]    ,							A_							:					Optional[Any]=-1       ):
						snake_case,					snake_case       :     str =    tf.split(A_    ,							2    ,							axis=A_       )
						return a * tf.math.sigmoid(A_       )


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

						def      A (       A_							:					Union[str, Any]       ):
												return tf.keras.activations.gelu(A_    ,							approximate=A_       )

						UpperCAmelCase    					=   tf.keras.activations.gelu





						UpperCAmelCase    					=   approximate_gelu_wrap
else:
						UpperCAmelCase    					=   _gelu
						UpperCAmelCase    					=   _gelu_new


UpperCAmelCase    					=   {
    "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 (       A_							:					Optional[Any]       ):
						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

def      A (       A_							:					list[int]    ,							A_							:					int       ):
						snake_case       :     list[list[int]] =    []
						snake_case       :     list[int] =    []
						snake_case       :     int =    0
						snake_case       :     int =    sum(A_       )
						create_state_space_tree(A_    ,							A_    ,							A_    ,							A_    ,							A_    ,							A_       )
						return result

def      A (       A_							:					list[int]    ,							A_							:					int    ,							A_							:					int    ,							A_							:					list[int]    ,							A_							:					list[list[int]]    ,							A_							:					int    ,							):
						if sum(A_       ) > max_sum or (remaining_nums_sum + sum(A_       )) < max_sum:
												return
						if sum(A_       ) == max_sum:
												result.append(A_       )
												return
						for index in range(A_    ,							len(A_       )       ):
												create_state_space_tree(
												    A_    ,							A_    ,							index + 1    ,							[*path, nums[index]]    ,							A_    ,							remaining_nums_sum - nums[index]    ,							)
UpperCAmelCase    					=   [3, 34, 4, 12, 5, 2]
UpperCAmelCase    					=   9
UpperCAmelCase    					=   generate_sum_of_subsets_soln(nums, max_sum)





print(*result)

