code: import math
  from collections import OrderedDict
  
  import torch
  from packaging import version
  from torch import Tensor, nn
  
  from .utils import logging
  
  
  a     :			Union[str, Any]					    =						logging.get_logger(__name__)
  
  class 				_lowercase						(    nn.Module ):
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      def __init__(						self   ):
         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     _a						(						self					,	lowerCamelCase__   ):
         return nn.functional.gelu(lowerCamelCase__					,	approximate="tanh"   )
  
  
  
  
  
  class 				_lowercase						(    nn.Module ):
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__   ):
         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 				_lowercase						(    nn.Module ):
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      def __init__(						self					,	lowerCamelCase__ = False   ):
         super().__init__()
         if use_gelu_python:
            lowerCAmelCase_:       str         =  self._gelu_python
         else:
            lowerCAmelCase_:       Dict         =  nn.functional.gelu
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__   ):
         return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0   )   ))
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__   ):
         return self.act(lowerCamelCase__   )
  
  
  
  
  
  class 				_lowercase						(    nn.Module ):
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__   ):
         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 				_lowercase						(    nn.Module ):
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__   ):
         return input * torch.sigmoid(1.7_0_2 * input   )
  
  
  
  
  
  class 				_lowercase						(    nn.Module ):
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      def __init__(						self					,	lowerCamelCase__					,	lowerCamelCase__   ):
         if min > max:
            raise ValueError(F'''min should be < max (got min: {min}, max: {max})'''   )
  
         super().__init__()
         lowerCAmelCase_:       str         =  min
         lowerCAmelCase_:       List[Any]         =  max
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__   ):
         return torch.clip(gelu(lowerCamelCase__   )					,	self.min					,	self.max   )
  
  
  
  
  
  class 				_lowercase						(    nn.Module ):
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      def __init__(						self   ):
         super().__init__()
         lowerCAmelCase_:       Any         =  math.sqrt(2 / math.pi   )
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__   ):
         return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.0_4_4_7_1_5 * torch.pow(lowerCamelCase__					,	3   ))   ))
  
  
  
  
  
  class 				_lowercase						(    nn.Module ):
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__   ):
         return nn.functional.silu(lowerCamelCase__   )
  
  
  
  
  
  class 				_lowercase						(    nn.Module ):
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      def __init__(						self   ):
         super().__init__()
         if version.parse(torch.__version__   ) < version.parse("1.9.0"   ):
            lowerCAmelCase_:       Optional[Any]         =  self._mish_python
         else:
            lowerCAmelCase_:       Optional[int]         =  nn.functional.mish
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__   ):
         return input * torch.tanh(nn.functional.softplus(lowerCamelCase__   )   )
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__   ):
         return self.act(lowerCamelCase__   )
  
  
  
  
  
  class 				_lowercase						(    nn.Module ):
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__   ):
         return input
  
  
  
  
  
  class 				_lowercase						(    nn.Module ):
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__=0.7_0_7_1_0_7					,	lowerCamelCase__=0.2_8_2_0_9_5   ):
         lowerCAmelCase_:       Any         =  (input - mu).div(sigma * math.sqrt(2.0   )   )
         return 0.5 * (1.0 + torch.erf(lowerCamelCase__   ))
  
  
  
  
  
  class 				_lowercase						(    nn.Module ):
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__   ):
         lowerCAmelCase_:       int         =  nn.functional.relu(lowerCamelCase__   )
         lowerCAmelCase_:       Optional[Any]         =  torch.square(lowerCamelCase__   )
         return squared
  
  
  
  
  
  class 				_lowercase						(    UpperCAmelCase__ ):
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      def __getitem__(						self					,	lowerCamelCase__   ):
         lowerCAmelCase_:       int         =  super().__getitem__(lowerCamelCase__   )
         lowerCAmelCase_:       List[str]         =  content if isinstance(lowerCamelCase__					,	lowerCamelCase__   ) else (content, {})
         return cls(**lowerCamelCase__   )
  
  
  
  
  
  a     :			Optional[int]					    =						{
      """gelu""": GELUActivation,
      """gelu_10""": (ClippedGELUActivation, {"""min""": -1_0, """max""": 1_0}),
      """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     :			List[Any]					    =						ClassInstantier(ACTaCLS)
  def 		snake_case__  (		lowercase				):
     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     :			Tuple					    =						get_activation("""gelu_new""")
  a     :			List[Any]					    =						get_activation("""gelu""")
  a     :			Optional[Any]					    =						get_activation("""gelu_fast""")
  a     :			Optional[int]					    =						get_activation("""quick_gelu""")
  a     :			str					    =						get_activation("""silu""")
  a     :			Any					    =						get_activation("""mish""")
  
  
  
  
  
  
  
  a     :			str					    =						get_activation("""linear""")
style_context: from .balanced_parentheses import balanced_parentheses
  from .stack import Stack
  def 		snake_case__  (		lowercase				):
     return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(lowercase       ,							-1				)
  def 		snake_case__  (		lowercase				):
     if not balanced_parentheses(lowercase				):
        raise ValueError("Mismatched parentheses"				)
     lowerCAmelCase_:       Stack[str]         =  Stack()
     lowerCAmelCase_:       Union[str, Any]         =  []
     for char in expression_str:
        if char.isalpha() or char.isdigit():
           postfix.append(lowercase				)
        elif char == "(":
           stack.push(lowercase				)
        elif char == ")":
           while not stack.is_empty() and stack.peek() != "(":
              postfix.append(stack.pop()				)
           stack.pop()
        else:
           while not stack.is_empty() and precedence(lowercase				) <= precedence(stack.peek()				):
              postfix.append(stack.pop()				)
           stack.push(lowercase				)
     while not stack.is_empty():
        postfix.append(stack.pop()				)
     return " ".join(lowercase				)
  
  
  if __name__ == "__main__":
   from doctest import testmod
  
   testmod()
   a     :			int					    =						"""a+b*(c^d-e)^(f+g*h)-i"""
  
   print("""Infix to Postfix Notation demonstration:\n""")
   print("""Infix notation: """ + expression)
   print("""Postfix notation: """ + infix_to_postfix(expression))