



'''simple docstring'''




import math

import tensorflow as tf
from packaging import version






def 		_lowerCAmelCase				(_lowercase			):

       """simple docstring"""

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

       return x * cdf






def 		_lowerCAmelCase				(_lowercase			):

       """simple docstring"""

       a__								= tf.convert_to_tensor(_snake_case			)
       a__								= tf.cast(math.pi   ,					x.dtype			)
       a__								= tf.cast(0.044715   ,					x.dtype			)
       a__								= 0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi			) * (x + coeff * tf.pow(_snake_case   ,					3			))			))

       return x * cdf






def 		_lowerCAmelCase				(_lowercase			):

       """simple docstring"""

       a__								= tf.convert_to_tensor(_snake_case			)

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






def 		_lowerCAmelCase				(_lowercase			):

       """simple docstring"""

       a__								= tf.convert_to_tensor(_snake_case			)
       a__								= tf.cast(0.044715   ,					x.dtype			)
       a__								= tf.cast(0.7978845608   ,					x.dtype			)

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






def 		_lowerCAmelCase				(_lowercase			):

       """simple docstring"""

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






def 		_lowerCAmelCase				(_lowercase			):

       """simple docstring"""

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






def 		_lowerCAmelCase				(_lowercase   ,					_lowercase=-1			):

       """simple docstring"""

       a__						,					a__								= tf.split(_snake_case   ,					2   ,					axis=_snake_case			)
       return a * tf.math.sigmoid(_snake_case			)


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






    def 		_lowerCAmelCase				(_lowercase			):

           """simple docstring"""

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

    UpperCamelCase_       :  List[Any]					=  tf.keras.activations.gelu


    UpperCamelCase_       :  Union[str, Any]					=  approximate_gelu_wrap
else:
    UpperCamelCase_       :  Optional[Any]					=  _gelu
    UpperCamelCase_       :  int					=  _gelu_new


UpperCamelCase_       :  Dict					=  {
    """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 		_lowerCAmelCase				(_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()			)}'			)








# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

_SCREAMING_SNAKE_CASE   =  TypeVar("T")






class 					SCREAMING_SNAKE_CASE_  (       Generic[T] ):
       """simple docstring"""


       def __init__(				self	:List[Any],      snake_case	:bool = True):



          """simple docstring"""

          _lowercase              ={}  # dictionary of lists
          _lowercase              =directed





       def 				UpperCamelCase__						(				self	:Optional[Any],      snake_case	:T,      snake_case	:T):



          """simple docstring"""

          if not self.directed:  # For undirected graphs
             # if both source vertex and destination vertex are both present in the
             # adjacency list, add destination vertex to source vertex list of adjacent
             # vertices and add source vertex to destination vertex list of adjacent
             # vertices.
             if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                self.adj_list[source_vertex].append(snake_case)
                self.adj_list[destination_vertex].append(snake_case)
             # if only source vertex is present in adjacency list, add destination vertex
             # to source vertex list of adjacent vertices, then create a new vertex with
             # destination vertex as key and assign a list containing the source vertex
             # as it's first adjacent vertex.
             elif source_vertex in self.adj_list:
                self.adj_list[source_vertex].append(snake_case)
                _lowercase              =[source_vertex]
             # if only destination vertex is present in adjacency list, add source vertex
             # to destination vertex list of adjacent vertices, then create a new vertex
             # with source vertex as key and assign a list containing the source vertex
             # as it's first adjacent vertex.
             elif destination_vertex in self.adj_list:
                self.adj_list[destination_vertex].append(snake_case)
                _lowercase              =[destination_vertex]
             # if both source vertex and destination vertex are not present in adjacency
             # list, create a new vertex with source vertex as key and assign a list
             # containing the destination vertex as it's first adjacent vertex also
             # create a new vertex with destination vertex as key and assign a list
             # containing the source vertex as it's first adjacent vertex.
             else:
                _lowercase              =[destination_vertex]
                _lowercase              =[source_vertex]
          else:  # For directed graphs
             # if both source vertex and destination vertex are present in adjacency
             # list, add destination vertex to source vertex list of adjacent vertices.
             if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                self.adj_list[source_vertex].append(snake_case)
             # if only source vertex is present in adjacency list, add destination
             # vertex to source vertex list of adjacent vertices and create a new vertex
             # with destination vertex as key, which has no adjacent vertex
             elif source_vertex in self.adj_list:
                self.adj_list[source_vertex].append(snake_case)
                _lowercase              =[]
             # if only destination vertex is present in adjacency list, create a new
             # vertex with source vertex as key and assign a list containing destination
             # vertex as first adjacent vertex
             elif destination_vertex in self.adj_list:
                _lowercase              =[destination_vertex]
             # if both source vertex and destination vertex are not present in adjacency
             # list, create a new vertex with source vertex as key and a list containing
             # destination vertex as it's first adjacent vertex. Then create a new vertex
             # with destination vertex as key, which has no adjacent vertex
             else:
                _lowercase              =[destination_vertex]
                _lowercase              =[]

          return self





       def __repr__(				self	:Optional[int]):



          """simple docstring"""

          return pformat(self.adj_list)


