



import math
from collections import OrderedDict

import torch
from packaging import version
from torch import Tensor, nn

from .utils import logging


UpperCAmelCase__			:   Union[str, Any]      			=    logging.get_logger(__name__)





class 							lowerCAmelCase_		(		nn.Module     ):







		def __init__(    self  :   Any					)       ->		Optional[Any]:


						'''simple docstring'''







						super().__init__()
						if version.parse(torch.__version__					) < version.parse('''1.12.0'''					):
										raise ImportError(
										    F'''You are using torch=={torch.__version__}, but torch>=1.12.0 is required to use '''
										    '''PytorchGELUTanh. Please upgrade torch.'''					)







		def 					a_  (    self  :   Dict       ,		UpperCAmelCase_  :   Tensor					)       ->		Tensor:


						'''simple docstring'''







						return nn.functional.gelu(UpperCAmelCase_       ,		approximate='''tanh'''					)





class 							lowerCAmelCase_		(		nn.Module     ):







		def 					a_  (    self  :   Optional[Any]       ,		UpperCAmelCase_  :   Tensor					)       ->		Tensor:


						'''simple docstring'''







						return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi					) * (input + 0.044_715 * torch.pow(UpperCAmelCase_       ,		3.0					))					))





class 							lowerCAmelCase_		(		nn.Module     ):







		def __init__(    self  :   List[str]       ,		UpperCAmelCase_  :   bool = False					)       ->		List[Any]:


						'''simple docstring'''







						super().__init__()
						if use_gelu_python:
										_UpperCAmelCase						:  Optional[int]     					=	self._gelu_python
						else:
										_UpperCAmelCase						:  Any     					=	nn.functional.gelu







		def 					a_  (    self  :   Any       ,		UpperCAmelCase_  :   Tensor					)       ->		Tensor:


						'''simple docstring'''







						return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0					)					))







		def 					a_  (    self  :   Optional[Any]       ,		UpperCAmelCase_  :   Tensor					)       ->		Tensor:


						'''simple docstring'''







						return self.act(UpperCAmelCase_					)





class 							lowerCAmelCase_		(		nn.Module     ):







		def 					a_  (    self  :   Optional[Any]       ,		UpperCAmelCase_  :   Tensor					)       ->		Tensor:


						'''simple docstring'''







						return 0.5 * input * (1.0 + torch.tanh(input * 0.7_978_845_608 * (1.0 + 0.044_715 * input * input)					))





class 							lowerCAmelCase_		(		nn.Module     ):







		def 					a_  (    self  :   Optional[Any]       ,		UpperCAmelCase_  :   Tensor					)       ->		Tensor:


						'''simple docstring'''







						return input * torch.sigmoid(1.702 * input					)





class 							lowerCAmelCase_		(		nn.Module     ):







		def __init__(    self  :   Dict       ,		UpperCAmelCase_  :   float       ,		UpperCAmelCase_  :   float					)       ->		Any:


						'''simple docstring'''







						if min > max:
										raise ValueError(F'''min should be < max (got min: {min}, max: {max})'''					)

						super().__init__()
						_UpperCAmelCase						:  Optional[int]     					=	min
						_UpperCAmelCase						:  str     					=	max







		def 					a_  (    self  :   Union[str, Any]       ,		UpperCAmelCase_  :   Tensor					)       ->		Tensor:


						'''simple docstring'''







						return torch.clip(gelu(UpperCAmelCase_					)       ,		self.min       ,		self.max					)





class 							lowerCAmelCase_		(		nn.Module     ):







		def __init__(    self  :   Dict					)       ->		Optional[int]:


						'''simple docstring'''







						super().__init__()
						_UpperCAmelCase						:  Tuple     					=	math.sqrt(2 / math.pi					)







		def 					a_  (    self  :   int       ,		UpperCAmelCase_  :   Tensor					)       ->		Tensor:


						'''simple docstring'''







						return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.044_715 * torch.pow(UpperCAmelCase_       ,		3					))					))





class 							lowerCAmelCase_		(		nn.Module     ):







		def 					a_  (    self  :   Optional[Any]       ,		UpperCAmelCase_  :   Tensor					)       ->		Tensor:


						'''simple docstring'''







						return nn.functional.silu(UpperCAmelCase_					)





class 							lowerCAmelCase_		(		nn.Module     ):







		def __init__(    self  :   Tuple					)       ->		List[Any]:


						'''simple docstring'''







						super().__init__()
						if version.parse(torch.__version__					) < version.parse('''1.9.0'''					):
										_UpperCAmelCase						:  List[Any]     					=	self._mish_python
						else:
										_UpperCAmelCase						:  int     					=	nn.functional.mish







		def 					a_  (    self  :   Optional[Any]       ,		UpperCAmelCase_  :   Tensor					)       ->		Tensor:


						'''simple docstring'''







						return input * torch.tanh(nn.functional.softplus(UpperCAmelCase_					)					)







		def 					a_  (    self  :   Optional[int]       ,		UpperCAmelCase_  :   Tensor					)       ->		Tensor:


						'''simple docstring'''







						return self.act(UpperCAmelCase_					)





class 							lowerCAmelCase_		(		nn.Module     ):







		def 					a_  (    self  :   Any       ,		UpperCAmelCase_  :   Tensor					)       ->		Tensor:


						'''simple docstring'''







						return input





class 							lowerCAmelCase_		(		nn.Module     ):







		def 					a_  (    self  :   Optional[Any]       ,		UpperCAmelCase_  :   Dict       ,		UpperCAmelCase_  :   int=0.707_107       ,		UpperCAmelCase_  :   List[str]=0.282_095					)       ->		Union[str, Any]:


						'''simple docstring'''







						_UpperCAmelCase						:  int     					=	(input - mu).div(sigma * math.sqrt(2.0					)					)
						return 0.5 * (1.0 + torch.erf(UpperCAmelCase_					))





class 							lowerCAmelCase_		(		nn.Module     ):







		def 					a_  (    self  :   List[Any]       ,		UpperCAmelCase_  :   int					)       ->		List[str]:


						'''simple docstring'''







						_UpperCAmelCase						:  Optional[Any]     					=	nn.functional.relu(UpperCAmelCase_					)
						_UpperCAmelCase						:  Tuple     					=	torch.square(UpperCAmelCase_					)
						return squared





class 							lowerCAmelCase_		(		lowercase_     ):







		def __getitem__(    self  :   int       ,		UpperCAmelCase_  :   Optional[Any]					)       ->		int:


						'''simple docstring'''







						_UpperCAmelCase						:  Dict     					=	super().__getitem__(UpperCAmelCase_					)
						_UpperCAmelCase					, _UpperCAmelCase						:  str     					=	content if isinstance(UpperCAmelCase_       ,		UpperCAmelCase_					) else (content, {})
						return cls(**UpperCAmelCase_					)
UpperCAmelCase__			:   Union[str, Any]      			=    {
    'gelu': GELUActivation,
    'gelu_10': (ClippedGELUActivation, {'min': -10, 'max': 10}),
    'gelu_fast': FastGELUActivation,
    'gelu_new': NewGELUActivation,
    'gelu_python': (GELUActivation, {'use_gelu_python': True}),
    'gelu_pytorch_tanh': PytorchGELUTanh,
    'gelu_accurate': AccurateGELUActivation,
    'laplace': LaplaceActivation,
    'linear': LinearActivation,
    'mish': MishActivation,
    'quick_gelu': QuickGELUActivation,
    'relu': nn.ReLU,
    'relu2': ReLUSquaredActivation,
    'relu6': nn.ReLUa,
    'sigmoid': nn.Sigmoid,
    'silu': SiLUActivation,
    'swish': SiLUActivation,
    'tanh': nn.Tanh,
}
UpperCAmelCase__			:   List[str]      			=    ClassInstantier(ACTaCLS)


def 	_A	(   _UpperCamelCase			):
				if activation_string in ACTaFN:
								return ACTaFN[activation_string]
				else:
								raise KeyError(F'''function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()			)}'''			)


# For backwards compatibility with: from activations import gelu_python
UpperCAmelCase__			:   Optional[Any]      			=    get_activation('gelu_python')
UpperCAmelCase__			:   List[str]      			=    get_activation('gelu_new')
UpperCAmelCase__			:   Union[str, Any]      			=    get_activation('gelu')
UpperCAmelCase__			:   Union[str, Any]      			=    get_activation('gelu_fast')
UpperCAmelCase__			:   str      			=    get_activation('quick_gelu')
UpperCAmelCase__			:   Dict      			=    get_activation('silu')
UpperCAmelCase__			:   Union[str, Any]      			=    get_activation('mish')






UpperCAmelCase__			:   int      			=    get_activation('linear')





class 							lowerCAmelCase_		:







		def __init__(    self  :   int       ,		UpperCAmelCase_  :   int					)       ->		None:


						'''simple docstring'''







						_UpperCAmelCase						:  Tuple     					=	data
						_UpperCAmelCase						:  BinaryTreeNode | None     					=	None
						_UpperCAmelCase						:  BinaryTreeNode | None     					=	None


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase			):
				if node is None:
								_UpperCAmelCase						:  Union[str, Any]     					=	BinaryTreeNode(_UpperCamelCase			)
								return node

				# binary search tree is not empty,
				# so we will insert it into the tree
				# if new_value is less than value of data in node,
				#  add it to left subtree and proceed recursively
				if new_value < node.data:
								_UpperCAmelCase						:  Any     					=	insert(node.left_child      ,					_UpperCamelCase			)
				else:
								# if new_value is greater than value of data in node,
								#  add it to right subtree and proceed recursively
								_UpperCAmelCase						:  List[Any]     					=	insert(node.right_child      ,					_UpperCamelCase			)
				return node


def 	_A	(   _UpperCamelCase			):  # if node is None,return
				if node:
								_UpperCAmelCase						:  str     					=	inorder(node.left_child			)
								_UpperCAmelCase						:  Union[str, Any]     					=	[*inorder_array, node.data]
								_UpperCAmelCase						:  Optional[int]     					=	inorder_array + inorder(node.right_child			)
				else:
								_UpperCAmelCase						:  Tuple     					=	[]
				return inorder_array


def 	_A	(   ):
				_UpperCAmelCase						:  Dict     					=	insert(_UpperCamelCase      ,					15			)
				insert(_UpperCamelCase      ,					10			)
				insert(_UpperCamelCase      ,					25			)
				insert(_UpperCamelCase      ,					6			)
				insert(_UpperCamelCase      ,					14			)
				insert(_UpperCamelCase      ,					20			)
				insert(_UpperCamelCase      ,					60			)
				return root


def 	_A	(   ):
				# main function
				_UpperCAmelCase						:  Tuple     					=	make_tree()
				print('''Printing values of binary search tree in Inorder Traversal.'''			)
				inorder(_UpperCamelCase			)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()
