



"""simple docstring"""







def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       int = 3_0   ):
						"""simple docstring"""






						snake_case      : List[str]							=   exponent + 2
						snake_case      : Optional[Any]							=   (1 + 5**0.5) / 2
						snake_case      : Dict							=   (phi**fibonacci_index - (phi - 1) ** fibonacci_index) / 5**0.5

						return int(_UpperCamelCase   )


if __name__ == "__main__":
			print(F'''{solution() = }''')








"""simple docstring"""







import math
from collections import OrderedDict

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

from .utils import logging


A    =	logging.get_logger(__name__)

class 			_a     (   nn.Module):



			def __init__( self       :							Union[str, Any]		)       ->				str:
									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 							__lowercase				( self       :							str      ,  _lowercase       :							Tensor		)       ->				Tensor:
									return nn.functional.gelu(_lowercase      ,  approximate="tanh"		)



class 			_a     (   nn.Module):



			def 							__lowercase				( self       :							Tuple      ,  _lowercase       :							Tensor		)       ->				Tensor:
									return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi		) * (input + 0.044715 * torch.pow(_lowercase      ,  3.0		))		))



class 			_a     (   nn.Module):



			def __init__( self       :							Tuple      ,  _lowercase       :							bool = False		)       ->				Optional[Any]:
									super().__init__()
									if use_gelu_python:
															snake_case      : Tuple							=   self._gelu_python
									else:
															snake_case      : Any							=   nn.functional.gelu



			def 							__lowercase				( self       :							Any      ,  _lowercase       :							Tensor		)       ->				Tensor:
									return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0		)		))



			def 							__lowercase				( self       :							Optional[int]      ,  _lowercase       :							Tensor		)       ->				Tensor:
									return self.act(_lowercase		)



class 			_a     (   nn.Module):



			def 							__lowercase				( self       :							Union[str, Any]      ,  _lowercase       :							Tensor		)       ->				Tensor:
									return 0.5 * input * (1.0 + torch.tanh(input * 0.7978845608 * (1.0 + 0.044715 * input * input)		))



class 			_a     (   nn.Module):



			def 							__lowercase				( self       :							Dict      ,  _lowercase       :							Tensor		)       ->				Tensor:
									return input * torch.sigmoid(1.702 * input		)



class 			_a     (   nn.Module):



			def __init__( self       :							Union[str, Any]      ,  _lowercase       :							float      ,  _lowercase       :							float		)       ->				Union[str, Any]:
									if min > max:
															raise ValueError(F'''min should be < max (got min: {min}, max: {max})'''		)

									super().__init__()
									snake_case      : str							=   min
									snake_case      : Dict							=   max



			def 							__lowercase				( self       :							str      ,  _lowercase       :							Tensor		)       ->				Tensor:
									return torch.clip(gelu(_lowercase		)      ,  self.min      ,  self.max		)



class 			_a     (   nn.Module):



			def __init__( self       :							str		)       ->				Tuple:
									super().__init__()
									snake_case      : int							=   math.sqrt(2 / math.pi		)



			def 							__lowercase				( self       :							Union[str, Any]      ,  _lowercase       :							Tensor		)       ->				Tensor:
									return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.044715 * torch.pow(_lowercase      ,  3		))		))



class 			_a     (   nn.Module):



			def 							__lowercase				( self       :							int      ,  _lowercase       :							Tensor		)       ->				Tensor:
									return nn.functional.silu(_lowercase		)



class 			_a     (   nn.Module):



			def __init__( self       :							Union[str, Any]		)       ->				Optional[Any]:
									super().__init__()
									if version.parse(torch.__version__		) < version.parse("1.9.0"		):
															snake_case      : Union[str, Any]							=   self._mish_python
									else:
															snake_case      : Union[str, Any]							=   nn.functional.mish



			def 							__lowercase				( self       :							str      ,  _lowercase       :							Tensor		)       ->				Tensor:
									return input * torch.tanh(nn.functional.softplus(_lowercase		)		)



			def 							__lowercase				( self       :							Tuple      ,  _lowercase       :							Tensor		)       ->				Tensor:
									return self.act(_lowercase		)



class 			_a     (   nn.Module):



			def 							__lowercase				( self       :							List[Any]      ,  _lowercase       :							Tensor		)       ->				Tensor:
									return input



class 			_a     (   nn.Module):



			def 							__lowercase				( self       :							Optional[Any]      ,  _lowercase       :							Optional[Any]      ,  _lowercase       :							List[str]=0.707107      ,  _lowercase       :							Dict=0.282095		)       ->				int:
									snake_case      : Any							=   (input - mu).div(sigma * math.sqrt(2.0		)		)
									return 0.5 * (1.0 + torch.erf(_lowercase		))



class 			_a     (   nn.Module):



			def 							__lowercase				( self       :							str      ,  _lowercase       :							List[str]		)       ->				str:
									snake_case      : Optional[Any]							=   nn.functional.relu(_lowercase		)
									snake_case      : List[str]							=   torch.square(_lowercase		)
									return squared



class 			_a     (   SCREAMING_SNAKE_CASE__):



			def __getitem__( self       :							List[str]      ,  _lowercase       :							Any		)       ->				int:
									snake_case      : List[str]							=   super().__getitem__(_lowercase		)
									snake_case						, snake_case      : Optional[Any]							=   content if isinstance(_lowercase      ,  _lowercase		) else (content, {})
									return cls(**_lowercase		)



A    =	{
    'gelu': GELUActivation,
    'gelu_10': (ClippedGELUActivation, {'min': -1_0, 'max': 1_0}),
    '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,
}
A    =	ClassInstantier(ACTaCLS)

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       Tuple   ):
						"""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()   )}'''   )


# For backwards compatibility with: from activations import gelu_python
A    =	get_activation('gelu_python')
A    =	get_activation('gelu_new')
A    =	get_activation('gelu')
A    =	get_activation('gelu_fast')
A    =	get_activation('quick_gelu')
A    =	get_activation('silu')
A    =	get_activation('mish')
A    =	get_activation('linear')



