




"""simple docstring"""



import math

import tensorflow as tf
from packaging import version





def  _snake_case     (    UpperCAmelCase_  :					Union[str, Any]			):
     A__										=  tf.convert_to_tensor(UpperCAmelCase_			)
     A__										=  0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0			)					,   x.dtype			)			))

     return x * cdf





def  _snake_case     (    UpperCAmelCase_  :					Any			):
     A__										=  tf.convert_to_tensor(UpperCAmelCase_			)
     A__										=  tf.cast(math.pi					,   x.dtype			)
     A__										=  tf.cast(0.04_47_15					,   x.dtype			)
     A__										=  0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi			) * (x + coeff * tf.pow(UpperCAmelCase_					,   3			))			))

     return x * cdf





def  _snake_case     (    UpperCAmelCase_  :					Optional[Any]			):
     A__										=  tf.convert_to_tensor(UpperCAmelCase_			)

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





def  _snake_case     (    UpperCAmelCase_  :					int			):
     A__										=  tf.convert_to_tensor(UpperCAmelCase_			)
     A__										=  tf.cast(0.04_47_15					,   x.dtype			)
     A__										=  tf.cast(0.79_78_84_56_08					,   x.dtype			)

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





def  _snake_case     (    UpperCAmelCase_  :					Dict			):
     A__										=  tf.convert_to_tensor(UpperCAmelCase_			)
     A__										=  tf.cast(1.7_02					,   x.dtype			)
     return x * tf.math.sigmoid(coeff * x			)





def  _snake_case     (    UpperCAmelCase_  :					Optional[int]			):
     return tf.clip_by_value(_gelu(UpperCAmelCase_			)					,   -10					,   10			)





def  _snake_case     (    UpperCAmelCase_  :					Tuple					,   UpperCAmelCase_  :					Union[str, Any]=-1			):
     A__			,				A__										=  tf.split(UpperCAmelCase_					,   2					,   axis=UpperCAmelCase_			)
     return a * tf.math.sigmoid(UpperCAmelCase_			)


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





 def  _snake_case     (    UpperCAmelCase_  :					Any			):
      return tf.keras.activations.gelu(UpperCAmelCase_					,   approximate=UpperCAmelCase_			)

 SCREAMING_SNAKE_CASE_					:	Tuple							  =						tf.keras.activations.gelu

 SCREAMING_SNAKE_CASE_					:	Dict							  =						approximate_gelu_wrap
else:
 SCREAMING_SNAKE_CASE_					:	Optional[int]							  =						_gelu
 SCREAMING_SNAKE_CASE_					:	Optional[int]							  =						_gelu_new


SCREAMING_SNAKE_CASE_					:	Union[str, 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  _snake_case     (    UpperCAmelCase_  :					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"""



def  _snake_case     (    UpperCAmelCase_  :					List[str]			):
     A__										=  [0] * len(UpperCAmelCase_			)
     A__										=  []
     A__										=  [1] * len(UpperCAmelCase_			)

     for values in graph.values():
          for i in values:
               indegree[i] += 1

     for i in range(len(UpperCAmelCase_			)			):
          if indegree[i] == 0:
               queue.append(UpperCAmelCase_			)

     while queue:
          A__										=  queue.pop(0			)
          for x in graph[vertex]:
               indegree[x] -= 1

               if long_dist[vertex] + 1 > long_dist[x]:
                    A__										=  long_dist[vertex] + 1

               if indegree[x] == 0:
                    queue.append(UpperCAmelCase_			)

     print(max(UpperCAmelCase_			)			)


# Adjacency list of Graph
SCREAMING_SNAKE_CASE_					:	Any							  =						{0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}

longest_distance(graph)






