


import math
from collections import OrderedDict

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

from .utils import logging


lowerCAmelCase__       :	Dict     				=logging.get_logger(__name__)







class      __lowercase  (nn.Module   ):





							"""simple docstring"""







							def __init__(	self    ):





										"""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  UpperCamelCase__			(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




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







class      __lowercase  (nn.Module   ):





							"""simple docstring"""







							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




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







class      __lowercase  (nn.Module   ):





							"""simple docstring"""







							def __init__(	self       ,		lowerCAmelCase__ = False    ):





										"""simple docstring"""




										super().__init__()
										if use_gelu_python:
													SCREAMING_SNAKE_CASE_						:			Dict       						= self._gelu_python
										else:
													SCREAMING_SNAKE_CASE_						:			str       						= nn.functional.gelu






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




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






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										return self.act(lowerCAmelCase__    )







class      __lowercase  (nn.Module   ):





							"""simple docstring"""







							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




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







class      __lowercase  (nn.Module   ):





							"""simple docstring"""







							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										return input * torch.sigmoid(1.702 * input    )







class      __lowercase  (nn.Module   ):





							"""simple docstring"""







							def __init__(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__    ):





										"""simple docstring"""




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

										super().__init__()
										SCREAMING_SNAKE_CASE_						:			str       						= min
										SCREAMING_SNAKE_CASE_						:			int       						= max






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




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







class      __lowercase  (nn.Module   ):





							"""simple docstring"""







							def __init__(	self    ):





										"""simple docstring"""




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






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




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







class      __lowercase  (nn.Module   ):





							"""simple docstring"""







							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										return nn.functional.silu(lowerCAmelCase__    )







class      __lowercase  (nn.Module   ):





							"""simple docstring"""







							def __init__(	self    ):





										"""simple docstring"""




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






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




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






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										return self.act(lowerCAmelCase__    )







class      __lowercase  (nn.Module   ):





							"""simple docstring"""







							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										return input







class      __lowercase  (nn.Module   ):





							"""simple docstring"""







							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__=0.707_107       ,		lowerCAmelCase__=0.282_095    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= (input - mu).div(sigma * math.sqrt(2.0    )    )
										return 0.5 * (1.0 + torch.erf(lowerCAmelCase__    ))







class      __lowercase  (nn.Module   ):





							"""simple docstring"""







							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Dict       						= nn.functional.relu(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= torch.square(lowerCAmelCase__    )
										return squared







class      __lowercase  (__SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







							def __getitem__(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




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







lowerCAmelCase__       :	List[str]     				={
    '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,
}
lowerCAmelCase__       :	Any     				=ClassInstantier(ACTaCLS)






def 		a__       (							A__							):
			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
lowerCAmelCase__       :	Optional[Any]     				=get_activation('gelu_python')
lowerCAmelCase__       :	List[Any]     				=get_activation('gelu_new')
lowerCAmelCase__       :	str     				=get_activation('gelu')
lowerCAmelCase__       :	Optional[int]     				=get_activation('gelu_fast')
lowerCAmelCase__       :	Tuple     				=get_activation('quick_gelu')
lowerCAmelCase__       :	Optional[int]     				=get_activation('silu')
lowerCAmelCase__       :	int     				=get_activation('mish')



lowerCAmelCase__       :	List[str]     				=get_activation('linear')









def 		a__       (							A__ = 2_0							):

			try:
						SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= int(A__							)
			except (TypeError, ValueError):
						raise TypeError('Parameter n must be int or castable to int.'							)
			if n <= 0:
						raise ValueError('Parameter n must be greater than or equal to one.'							)
			SCREAMING_SNAKE_CASE_						:			int       						= 0
			while 1:
						i += n * (n - 1)
						SCREAMING_SNAKE_CASE_						:			List[str]       						= 0
						for j in range(2,	A__							):
									if i % j != 0:
												SCREAMING_SNAKE_CASE_						:			str       						= 1
												break
						if nfound == 0:
									if i == 0:
												SCREAMING_SNAKE_CASE_						:			Tuple       						= 1
									return i
			return None


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





