





'''simple docstring'''






import math
from collections import OrderedDict

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

from .utils import logging


A :			List[str]							   =      logging.get_logger(__name__)







class       lowerCamelCase       (    nn.Module	):


					def __init__(       self						:	Tuple							):





											'''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  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							,  __snake_case						:	Tensor							):





											'''simple docstring'''





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







class       lowerCamelCase       (    nn.Module	):


					def  SCREAMING_SNAKE_CASE_		(       self						:	Any							,  __snake_case						:	Tensor							):





											'''simple docstring'''





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







class       lowerCamelCase       (    nn.Module	):


					def __init__(       self						:	int							,  __snake_case						:	bool = False							):





											'''simple docstring'''





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


					def  SCREAMING_SNAKE_CASE_		(       self						:	int							,  __snake_case						:	Tensor							):





											'''simple docstring'''





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




					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							,  __snake_case						:	Tensor							):





											'''simple docstring'''





											return self.act(__snake_case							)







class       lowerCamelCase       (    nn.Module	):


					def  SCREAMING_SNAKE_CASE_		(       self						:	int							,  __snake_case						:	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  SCREAMING_SNAKE_CASE_		(       self						:	Union[str, Any]							,  __snake_case						:	Tensor							):





											'''simple docstring'''





											return input * torch.sigmoid(1.702 * input							)







class       lowerCamelCase       (    nn.Module	):


					def __init__(       self						:	List[Any]							,  __snake_case						:	float							,  __snake_case						:	float							):





											'''simple docstring'''





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

											super().__init__()
											_snake_case: int	       =		min
											_snake_case: List[str]	       =		max




					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							,  __snake_case						:	Tensor							):





											'''simple docstring'''





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







class       lowerCamelCase       (    nn.Module	):


					def __init__(       self						:	Any							):





											'''simple docstring'''





											super().__init__()
											_snake_case: Dict	       =		math.sqrt(2 / math.pi							)




					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[Any]							,  __snake_case						:	Tensor							):





											'''simple docstring'''





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







class       lowerCamelCase       (    nn.Module	):


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							,  __snake_case						:	Tensor							):





											'''simple docstring'''





											return nn.functional.silu(__snake_case							)







class       lowerCamelCase       (    nn.Module	):


					def __init__(       self						:	List[str]							):





											'''simple docstring'''





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


					def  SCREAMING_SNAKE_CASE_		(       self						:	Union[str, Any]							,  __snake_case						:	Tensor							):





											'''simple docstring'''





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




					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							,  __snake_case						:	Tensor							):





											'''simple docstring'''





											return self.act(__snake_case							)







class       lowerCamelCase       (    nn.Module	):


					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							,  __snake_case						:	Tensor							):





											'''simple docstring'''





											return input







class       lowerCamelCase       (    nn.Module	):


					def  SCREAMING_SNAKE_CASE_		(       self						:	Union[str, Any]							,  __snake_case						:	List[Any]							,  __snake_case						:	Optional[Any]=0.707_107							,  __snake_case						:	int=0.282_095							):





											'''simple docstring'''





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







class       lowerCamelCase       (    nn.Module	):


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							,  __snake_case						:	int							):





											'''simple docstring'''





											_snake_case: Dict	       =		nn.functional.relu(__snake_case							)
											_snake_case: int	       =		torch.square(__snake_case							)
											return squared







class       lowerCamelCase       (    __UpperCAmelCase	):


					def __getitem__(       self						:	Optional[Any]							,  __snake_case						:	List[str]							):





											'''simple docstring'''





											_snake_case: int	       =		super().__getitem__(__snake_case							)
											_snake_case       ,    _snake_case: List[Any]	       =		content if isinstance(__snake_case							,  __snake_case							) else (content, {})
											return cls(**__snake_case							)





A :			Optional[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,
}
A :			Dict							   =      ClassInstantier(ACTaCLS)





def    lowercase_   (		lowercase__    ) ->Optional[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()    )}'''    )


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



A :			str							   =      get_activation('linear')












'''simple docstring'''






def    lowercase_   (		lowercase__ = 400_0000    ) ->int:

						if n <= 1:
												return 0
						_snake_case: Union[str, Any]	       =		0
						_snake_case: List[Any]	       =		2
						_snake_case: Any	       =		0
						while 4 * b + a <= n:
												_snake_case       ,    _snake_case: int	       =		b, 4 * b + a
												count += a
						return count + b


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





