




'''simple docstring'''


import math
from collections import OrderedDict

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

from .utils import logging


_snake_case      :      int   							=		logging.get_logger(__name__)

class 			A    (		nn.Module    ):







							def __init__(  self       :  Optional[int]					)			->							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    __lowerCAmelCase			(  self       :  List[Any]							,			lowerCAmelCase_       :  Tensor					)			->							Tensor:


														"""simple docstring"""


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



class 			A    (		nn.Module    ):







							def    __lowerCAmelCase			(  self       :  Any							,			lowerCAmelCase_       :  Tensor					)			->							Tensor:


														"""simple docstring"""


														return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi					) * (input + 0.0_4_4_7_1_5 * torch.pow(lowerCAmelCase_							,			3.0					))					))



class 			A    (		nn.Module    ):







							def __init__(  self       :  Optional[int]							,			lowerCAmelCase_       :  bool = False					)			->							Optional[int]:


														"""simple docstring"""


														super().__init__()
														if use_gelu_python:
																					_a	     =				self._gelu_python
														else:
																					_a	     =				nn.functional.gelu







							def    __lowerCAmelCase			(  self       :  Optional[int]							,			lowerCAmelCase_       :  Tensor					)			->							Tensor:


														"""simple docstring"""


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







							def    __lowerCAmelCase			(  self       :  Tuple							,			lowerCAmelCase_       :  Tensor					)			->							Tensor:


														"""simple docstring"""


														return self.act(lowerCAmelCase_					)



class 			A    (		nn.Module    ):







							def    __lowerCAmelCase			(  self       :  List[Any]							,			lowerCAmelCase_       :  Tensor					)			->							Tensor:


														"""simple docstring"""


														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 			A    (		nn.Module    ):







							def    __lowerCAmelCase			(  self       :  Dict							,			lowerCAmelCase_       :  Tensor					)			->							Tensor:


														"""simple docstring"""


														return input * torch.sigmoid(1.7_0_2 * input					)



class 			A    (		nn.Module    ):







							def __init__(  self       :  Optional[int]							,			lowerCAmelCase_       :  float							,			lowerCAmelCase_       :  float					)			->							str:


														"""simple docstring"""


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

														super().__init__()
														_a	     =				min
														_a	     =				max







							def    __lowerCAmelCase			(  self       :  Union[str, Any]							,			lowerCAmelCase_       :  Tensor					)			->							Tensor:


														"""simple docstring"""


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



class 			A    (		nn.Module    ):







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


														"""simple docstring"""


														super().__init__()
														_a	     =				math.sqrt(2 / math.pi					)







							def    __lowerCAmelCase			(  self       :  Optional[Any]							,			lowerCAmelCase_       :  Tensor					)			->							Tensor:


														"""simple docstring"""


														return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.0_4_4_7_1_5 * torch.pow(lowerCAmelCase_							,			3					))					))



class 			A    (		nn.Module    ):







							def    __lowerCAmelCase			(  self       :  Union[str, Any]							,			lowerCAmelCase_       :  Tensor					)			->							Tensor:


														"""simple docstring"""


														return nn.functional.silu(lowerCAmelCase_					)



class 			A    (		nn.Module    ):







							def __init__(  self       :  Union[str, Any]					)			->							int:


														"""simple docstring"""


														super().__init__()
														if version.parse(torch.__version__					) < version.parse('''1.9.0'''					):
																					_a	     =				self._mish_python
														else:
																					_a	     =				nn.functional.mish







							def    __lowerCAmelCase			(  self       :  List[str]							,			lowerCAmelCase_       :  Tensor					)			->							Tensor:


														"""simple docstring"""


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







							def    __lowerCAmelCase			(  self       :  List[Any]							,			lowerCAmelCase_       :  Tensor					)			->							Tensor:


														"""simple docstring"""


														return self.act(lowerCAmelCase_					)



class 			A    (		nn.Module    ):







							def    __lowerCAmelCase			(  self       :  int							,			lowerCAmelCase_       :  Tensor					)			->							Tensor:


														"""simple docstring"""


														return input



class 			A    (		nn.Module    ):







							def    __lowerCAmelCase			(  self       :  List[str]							,			lowerCAmelCase_       :  str							,			lowerCAmelCase_       :  Any=0.7_0_7_1_0_7							,			lowerCAmelCase_       :  Tuple=0.2_8_2_0_9_5					)			->							List[Any]:


														"""simple docstring"""


														_a	     =				(input - mu).div(sigma * math.sqrt(2.0					)					)
														return 0.5 * (1.0 + torch.erf(lowerCAmelCase_					))



class 			A    (		nn.Module    ):







							def    __lowerCAmelCase			(  self       :  Any							,			lowerCAmelCase_       :  str					)			->							Tuple:


														"""simple docstring"""


														_a	     =				nn.functional.relu(lowerCAmelCase_					)
														_a	     =				torch.square(lowerCAmelCase_					)
														return squared



class 			A    (		_a    ):







							def __getitem__(  self       :  List[Any]							,			lowerCAmelCase_       :  Any					)			->							int:


														"""simple docstring"""


														_a	     =				super().__getitem__(lowerCAmelCase_					)
														_a     ,							_a	     =				content if isinstance(lowerCAmelCase_							,			lowerCAmelCase_					) else (content, {})
														return cls(**lowerCAmelCase_					)



_snake_case      :      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,
}
_snake_case      :      Any   							=		ClassInstantier(ACTaCLS)
def 		snake_case_    (UpperCamelCase       :  int      ):





							'''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
_snake_case      :      Union[str, Any]   							=		get_activation('gelu_python')
_snake_case      :      List[str]   							=		get_activation('gelu_new')
_snake_case      :      str   							=		get_activation('gelu')
_snake_case      :      Optional[Any]   							=		get_activation('gelu_fast')
_snake_case      :      Any   							=		get_activation('quick_gelu')
_snake_case      :      str   							=		get_activation('silu')
_snake_case      :      List[str]   							=		get_activation('mish')

_snake_case      :      int   							=		get_activation('linear')







'''simple docstring'''


from __future__ import annotations
def 		snake_case_    (UpperCamelCase       :  int      ):





							'''simple docstring'''


							_a	     =				[0] * max_one

							for i in range(0 ,						UpperCamelCase      ):
														_a	     =				i

							for i in range(2 ,						UpperCamelCase      ):
														if totients[i] == i:
																					for j in range(UpperCamelCase ,						UpperCamelCase ,						UpperCamelCase      ):
																												totients[j] -= totients[j] // i

							return totients
def 		snake_case_    (UpperCamelCase       :  int ,						UpperCamelCase       :  int      ):





							'''simple docstring'''


							return sorted(str(UpperCamelCase      )      ) == sorted(str(UpperCamelCase      )      )
def 		snake_case_    (UpperCamelCase       :  int = 1000_0000      ):





							'''simple docstring'''


							_a	     =				1  # i
							_a	     =				0  # φ(i)
							_a	     =				get_totients(max_n + 1      )

							for i in range(2 ,						max_n + 1      ):
														_a	     =				totients[i]

														if i * min_denominator < min_numerator * t and has_same_digits(UpperCamelCase ,						UpperCamelCase      ):
																					_a	     =				i
																					_a	     =				t

							return min_numerator


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

