




import math
from collections import OrderedDict

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

from .utils import logging


__lowerCamelCase :    Optional[Any]      						=      logging.get_logger(__name__)


class  lowerCamelCase     (		nn.Module    ):






			'''simple docstring'''





			def __init__(			self      : str	)    -> 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 				UpperCAmelCase__							(			self      : str							,    lowerCamelCase_      : Optional[Any]	)    -> Tensor:
								return nn.functional.gelu(__UpperCamelCase							,    approximate='''tanh'''	)






class  lowerCamelCase     (		nn.Module    ):






			'''simple docstring'''





			def 				UpperCAmelCase__							(			self      : int							,    lowerCamelCase_      : int	)    -> Tensor:
								return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi	) * (input + 0.0_4_4_7_1_5 * torch.pow(__UpperCamelCase							,    3.0	))	))






class  lowerCamelCase     (		nn.Module    ):






			'''simple docstring'''





			def __init__(			self      : str							,    lowerCamelCase_      : Any = False	)    -> Tuple:
								super().__init__()
								if use_gelu_python:
													__magic_name__				:						Optional[int]     				=				self._gelu_python
								else:
													__magic_name__				:						List[str]     				=				nn.functional.gelu

			def 				UpperCAmelCase__							(			self      : Tuple							,    lowerCamelCase_      : Union[str, Any]	)    -> Tensor:
								return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0	)	))


			def 				UpperCAmelCase__							(			self      : Union[str, Any]							,    lowerCamelCase_      : List[str]	)    -> Tensor:
								return self.act(__UpperCamelCase	)






class  lowerCamelCase     (		nn.Module    ):






			'''simple docstring'''





			def 				UpperCAmelCase__							(			self      : List[str]							,    lowerCamelCase_      : Optional[Any]	)    -> Tensor:
								return 0.5 * input * (1.0 + torch.tanh(input * 0.7_9_7_8_8_4_5_6_0_8 * (1.0 + 0.0_4_4_7_1_5 * input * input)	))






class  lowerCamelCase     (		nn.Module    ):






			'''simple docstring'''





			def 				UpperCAmelCase__							(			self      : str							,    lowerCamelCase_      : List[str]	)    -> Tensor:
								return input * torch.sigmoid(1.7_0_2 * input	)






class  lowerCamelCase     (		nn.Module    ):






			'''simple docstring'''





			def __init__(			self      : Dict							,    lowerCamelCase_      : int							,    lowerCamelCase_      : str	)    -> List[Any]:
								if min > max:
													raise ValueError(F'''min should be < max (got min: {min}, max: {max})'''	)

								super().__init__()
								__magic_name__				:						Dict     				=				min
								__magic_name__				:						List[Any]     				=				max


			def 				UpperCAmelCase__							(			self      : List[Any]							,    lowerCamelCase_      : Tuple	)    -> Tensor:
								return torch.clip(gelu(__UpperCamelCase	)							,    self.min							,    self.max	)






class  lowerCamelCase     (		nn.Module    ):






			'''simple docstring'''





			def __init__(			self      : Any	)    -> Optional[Any]:
								super().__init__()
								__magic_name__				:						Dict     				=				math.sqrt(2 / math.pi	)


			def 				UpperCAmelCase__							(			self      : List[str]							,    lowerCamelCase_      : Optional[Any]	)    -> Tensor:
								return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.0_4_4_7_1_5 * torch.pow(__UpperCamelCase							,    3	))	))






class  lowerCamelCase     (		nn.Module    ):






			'''simple docstring'''





			def 				UpperCAmelCase__							(			self      : int							,    lowerCamelCase_      : Tuple	)    -> Tensor:
								return nn.functional.silu(__UpperCamelCase	)






class  lowerCamelCase     (		nn.Module    ):






			'''simple docstring'''





			def __init__(			self      : Optional[int]	)    -> Optional[Any]:
								super().__init__()
								if version.parse(torch.__version__	) < version.parse('''1.9.0'''	):
													__magic_name__				:						Any     				=				self._mish_python
								else:
													__magic_name__				:						Optional[int]     				=				nn.functional.mish

			def 				UpperCAmelCase__							(			self      : Union[str, Any]							,    lowerCamelCase_      : Optional[int]	)    -> Tensor:
								return input * torch.tanh(nn.functional.softplus(__UpperCamelCase	)	)


			def 				UpperCAmelCase__							(			self      : int							,    lowerCamelCase_      : str	)    -> Tensor:
								return self.act(__UpperCamelCase	)






class  lowerCamelCase     (		nn.Module    ):






			'''simple docstring'''





			def 				UpperCAmelCase__							(			self      : int							,    lowerCamelCase_      : Dict	)    -> Tensor:
								return input






class  lowerCamelCase     (		nn.Module    ):






			'''simple docstring'''





			def 				UpperCAmelCase__							(			self      : Tuple							,    lowerCamelCase_      : List[Any]							,    lowerCamelCase_      : Dict=0.7_0_7_1_0_7							,    lowerCamelCase_      : str=0.2_8_2_0_9_5	)    -> Union[str, Any]:
								__magic_name__				:						Dict     				=				(input - mu).div(sigma * math.sqrt(2.0	)	)
								return 0.5 * (1.0 + torch.erf(__UpperCamelCase	))






class  lowerCamelCase     (		nn.Module    ):






			'''simple docstring'''





			def 				UpperCAmelCase__							(			self      : Optional[int]							,    lowerCamelCase_      : List[str]	)    -> Any:
								__magic_name__				:						Optional[int]     				=				nn.functional.relu(__UpperCamelCase	)
								__magic_name__				:						Dict     				=				torch.square(__UpperCamelCase	)
								return squared






class  lowerCamelCase     (		lowerCamelCase__    ):






			'''simple docstring'''





			def __getitem__(			self      : Tuple							,    lowerCamelCase_      : Union[str, Any]	)    -> List[Any]:
								__magic_name__				:						str     				=				super().__getitem__(__UpperCamelCase	)
								__magic_name__				,     __magic_name__				:						Union[str, Any]     				=				content if isinstance(__UpperCamelCase							,    __UpperCamelCase	) else (content, {})
								return cls(**__UpperCamelCase	)






__lowerCamelCase :    Optional[int]      						=      {
    '''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 :    Optional[Any]      						=      ClassInstantier(ACTaCLS)







def 		lowercase__ (		__A:						str					):





					'''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
__lowerCamelCase :    List[Any]      						=      get_activation('''gelu_python''')
__lowerCamelCase :    Optional[Any]      						=      get_activation('''gelu_new''')
__lowerCamelCase :    Any      						=      get_activation('''gelu''')
__lowerCamelCase :    int      						=      get_activation('''gelu_fast''')
__lowerCamelCase :    int      						=      get_activation('''quick_gelu''')
__lowerCamelCase :    Dict      						=      get_activation('''silu''')
__lowerCamelCase :    Tuple      						=      get_activation('''mish''')
__lowerCamelCase :    int      						=      get_activation('''linear''')







'''simple docstring'''

def __UpperCamelCase (      __lowerCamelCase		:			float   ,	__lowerCamelCase		:			float   ,	__lowerCamelCase		:			float					)   ->  float:







     '''simple docstring'''






     if mass < 0:
          raise ValueError("The mass of the body cannot be negative"					)
     if radius <= 0:
          raise ValueError("The radius is always a positive non zero integer"					)
     return (mass * (velocity) ** 2) / radius


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)

