'''simple docstring'''




import math

import tensorflow as tf
from packaging import version






def 							snake_case__				(     _A:   List[str]					)       ->      List[str]:

		'''simple docstring'''


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

		return x * cdf






def 							snake_case__				(     _A:   Dict					)       ->      Dict:

		'''simple docstring'''


		lowerCAmelCase     =    tf.convert_to_tensor(_A					)
		lowerCAmelCase     =    tf.cast(math.pi ,  x.dtype					)
		lowerCAmelCase     =    tf.cast(0.044_715 ,  x.dtype					)
		lowerCAmelCase     =    0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi					) * (x + coeff * tf.pow(_A ,  3					))					))

		return x * cdf






def 							snake_case__				(     _A:   Dict					)       ->      Dict:

		'''simple docstring'''


		lowerCAmelCase     =    tf.convert_to_tensor(_A					)

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






def 							snake_case__				(     _A:   List[Any]					)       ->      Union[str, Any]:

		'''simple docstring'''


		lowerCAmelCase     =    tf.convert_to_tensor(_A					)
		lowerCAmelCase     =    tf.cast(0.044_715 ,  x.dtype					)
		lowerCAmelCase     =    tf.cast(0.7_978_845_608 ,  x.dtype					)

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






def 							snake_case__				(     _A:   Dict					)       ->      Tuple:

		'''simple docstring'''


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






def 							snake_case__				(     _A:   Tuple					)       ->      Dict:

		'''simple docstring'''


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






def 							snake_case__				(     _A:   Tuple ,  _A:   Tuple=-1					)       ->      Dict:

		'''simple docstring'''


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


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






						def 							snake_case__				(     _A:   Optional[int]					)       ->      List[str]:

								'''simple docstring'''


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

						__lowercase        =    tf.keras.activations.gelu




						__lowercase        =    approximate_gelu_wrap
else:
						__lowercase        =    _gelu
						__lowercase        =    _gelu_new


__lowercase        =    {
    '''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 							snake_case__				(     _A:   Union[str, Any]					)       ->      Optional[int]:

		'''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'''




# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.






def 							snake_case__				(     _A:   Dict					)       ->      str:

		'''simple docstring'''


		lowerCAmelCase     =    [False] * len(_A					)
		lowerCAmelCase     =    [-1] * len(_A					)

		def dfs(_A:   Dict ,  _A:   Tuple					):
				lowerCAmelCase     =    True
				lowerCAmelCase     =    c
				for u in graph[v]:
						if not visited[u]:
								dfs(_A ,  1 - c					)

		for i in range(len(_A					)					):
				if not visited[i]:
						dfs(_A ,  0					)

		for i in range(len(_A					)					):
				for j in graph[i]:
						if color[i] == color[j]:
								return False

		return True


# Adjacency list of graph
__lowercase        =    {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}




print(check_bipartite_dfs(graph))




