




import math
from collections import OrderedDict

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

from .utils import logging


UpperCamelCase__       				=	logging.get_logger(__name__)
class 							__lowercase   (			nn.Module    ):



     def __init__(  self   :							Union[str, Any]       ):
       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 				__magic_name__		(  self   :							Tuple      ,		lowercase__   :							Tensor       ):
       return nn.functional.gelu(lowercase__      ,		approximate='''tanh'''       )



class 							__lowercase   (			nn.Module    ):



     def 				__magic_name__		(  self   :							Tuple      ,		lowercase__   :							Tensor       ):
       return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi       ) * (input + 0.04_4715 * torch.pow(lowercase__      ,		3.0       ))       ))



class 							__lowercase   (			nn.Module    ):



     def __init__(  self   :							List[Any]      ,		lowercase__   :							bool = False       ):
       super().__init__()
       if use_gelu_python:
         a_		    =  self._gelu_python
       else:
         a_		    =  nn.functional.gelu



     def 				__magic_name__		(  self   :							List[str]      ,		lowercase__   :							Tensor       ):
       return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0       )       ))



     def 				__magic_name__		(  self   :							Dict      ,		lowercase__   :							Tensor       ):
       return self.act(lowercase__       )



class 							__lowercase   (			nn.Module    ):



     def 				__magic_name__		(  self   :							List[str]      ,		lowercase__   :							Tensor       ):
       return 0.5 * input * (1.0 + torch.tanh(input * 0.79_7884_5608 * (1.0 + 0.04_4715 * input * input)       ))



class 							__lowercase   (			nn.Module    ):



     def 				__magic_name__		(  self   :							Tuple      ,		lowercase__   :							Tensor       ):
       return input * torch.sigmoid(1.702 * input       )



class 							__lowercase   (			nn.Module    ):



     def __init__(  self   :							int      ,		lowercase__   :							float      ,		lowercase__   :							float       ):
       if min > max:
         raise ValueError(f"min should be < max (got min: {min}, max: {max})"       )

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



     def 				__magic_name__		(  self   :							List[str]      ,		lowercase__   :							Tensor       ):
       return torch.clip(gelu(lowercase__       )      ,		self.min      ,		self.max       )



class 							__lowercase   (			nn.Module    ):



     def __init__(  self   :							Optional[int]       ):
       super().__init__()
       a_		    =  math.sqrt(2 / math.pi       )



     def 				__magic_name__		(  self   :							Any      ,		lowercase__   :							Tensor       ):
       return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.04_4715 * torch.pow(lowercase__      ,		3       ))       ))



class 							__lowercase   (			nn.Module    ):



     def 				__magic_name__		(  self   :							Optional[int]      ,		lowercase__   :							Tensor       ):
       return nn.functional.silu(lowercase__       )



class 							__lowercase   (			nn.Module    ):



     def __init__(  self   :							Union[str, Any]       ):
       super().__init__()
       if version.parse(torch.__version__       ) < version.parse('''1.9.0'''       ):
         a_		    =  self._mish_python
       else:
         a_		    =  nn.functional.mish



     def 				__magic_name__		(  self   :							List[str]      ,		lowercase__   :							Tensor       ):
       return input * torch.tanh(nn.functional.softplus(lowercase__       )       )



     def 				__magic_name__		(  self   :							Dict      ,		lowercase__   :							Tensor       ):
       return self.act(lowercase__       )



class 							__lowercase   (			nn.Module    ):



     def 				__magic_name__		(  self   :							Optional[Any]      ,		lowercase__   :							Tensor       ):
       return input



class 							__lowercase   (			nn.Module    ):



     def 				__magic_name__		(  self   :							Optional[Any]      ,		lowercase__   :							Any      ,		lowercase__   :							Optional[int]=0.70_7107      ,		lowercase__   :							str=0.28_2095       ):
       a_		    =  (input - mu).div(sigma * math.sqrt(2.0       )       )
       return 0.5 * (1.0 + torch.erf(lowercase__       ))



class 							__lowercase   (			nn.Module    ):



     def 				__magic_name__		(  self   :							List[str]      ,		lowercase__   :							Any       ):
       a_		    =  nn.functional.relu(lowercase__       )
       a_		    =  torch.square(lowercase__       )
       return squared



class 							__lowercase   (			a__    ):



     def __getitem__(  self   :							Any      ,		lowercase__   :							List[str]       ):
       a_		    =  super().__getitem__(lowercase__       )
       a_    , a_		    =  content if isinstance(lowercase__      ,		lowercase__       ) else (content, {})
       return cls(**lowercase__       )



UpperCamelCase__       				=	{
    '''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,
}
UpperCamelCase__       				=	ClassInstantier(ACTaCLS)






def 				UpperCAmelCase__  (							_A			):






  """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
UpperCamelCase__       				=	get_activation('''gelu_python''')
UpperCamelCase__       				=	get_activation('''gelu_new''')
UpperCamelCase__       				=	get_activation('''gelu''')
UpperCamelCase__       				=	get_activation('''gelu_fast''')
UpperCamelCase__       				=	get_activation('''quick_gelu''')
UpperCamelCase__       				=	get_activation('''silu''')
UpperCamelCase__       				=	get_activation('''mish''')




UpperCamelCase__       				=	get_activation('''linear''')






import math






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  a_		    =  math.log(2      ,      10			) * number
  a_		    =  round((log_number - int(_A			))      ,      15			)

  return difference






def 				UpperCAmelCase__  (							_A = 678_910			):






  """simple docstring"""


  a_		    =  90
  a_		    =  0

  a_		    =  math.log(1.23      ,      10			)
  a_		    =  math.log(1.24      ,      10			)
  a_		    =  0

  while position < number:
    a_		    =  log_difference(_A			)

    if difference >= upper_limit:
      power_iterator += 93

    elif difference < lower_limit:
      power_iterator += 196

    else:
      a_		    =  power_iterator
      power_iterator += 196
      position += 1

  return previous_power


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  print(F"""{solution() = }""")
