




from __future__ import annotations
def 							__lowerCAmelCase			(UpperCAmelCase_ ,     UpperCAmelCase_		):
    a__			,		a__  			=							0, len(UpperCAmelCase_		) - 1
    while start_ind <= end_ind:
        if array[start_ind] == search_item:
            return start_ind
        elif array[end_ind] == search_item:
            return end_ind
        else:
            start_ind += 1
            end_ind -= 1
    # returns -1 if search_item is not found in array
    return -1


if __name__ == "__main__":
 print(double_linear_search(list(range(1_00)), 40))












import math
from collections import OrderedDict

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

from .utils import logging


SCREAMING_SNAKE_CASE_    :       Tuple       =							logging.get_logger(__name__)







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def __init__(   self			:							int     )      ->      Union[str, 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 					__SCREAMING_SNAKE_CASE		(   self			:							str    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return nn.functional.gelu(A    ,					approximate="""tanh"""     )







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def 					__SCREAMING_SNAKE_CASE		(   self			:							str    ,					A			:							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(A    ,					3.0     ))     ))







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def __init__(   self			:							Optional[Any]    ,					A			:							bool = False     )      ->      Optional[Any]:






         """simple docstring"""
         super().__init__()
         if use_gelu_python:
             a__  			=							self._gelu_python
         else:
             a__  			=							nn.functional.gelu

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict    ,					A			:							Tensor     )      ->      Tensor:






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






     def 					__SCREAMING_SNAKE_CASE		(   self			:							Union[str, Any]    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return self.act(A     )







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any    ,					A			:							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 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def 					__SCREAMING_SNAKE_CASE		(   self			:							List[Any]    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return input * torch.sigmoid(1.7_0_2 * input     )







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def __init__(   self			:							Tuple    ,					A			:							float    ,					A			:							float     )      ->      Optional[Any]:






         """simple docstring"""
         if min > max:
             raise ValueError(f"""min should be < max (got min: {min}, max: {max})"""     )

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






     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return torch.clip(gelu(A     )    ,					self.min    ,					self.max     )







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



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






         """simple docstring"""
         super().__init__()
         a__  			=							math.sqrt(2 / math.pi     )






     def 					__SCREAMING_SNAKE_CASE		(   self			:							int    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.0_4_4_7_1_5 * torch.pow(A    ,					3     ))     ))







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return nn.functional.silu(A     )







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



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






         """simple docstring"""
         super().__init__()
         if version.parse(torch.__version__     ) < version.parse("""1.9.0"""     ):
             a__  			=							self._mish_python
         else:
             a__  			=							nn.functional.mish

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return input * torch.tanh(nn.functional.softplus(A     )     )






     def 					__SCREAMING_SNAKE_CASE		(   self			:							Union[str, Any]    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return self.act(A     )







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[Any]    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return input







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def 					__SCREAMING_SNAKE_CASE		(   self			:							List[Any]    ,					A			:							str    ,					A			:							Dict=0.7_0_7_1_0_7    ,					A			:							Union[str, Any]=0.2_8_2_0_9_5     )      ->      int:






         """simple docstring"""
         a__  			=							(input - mu).div(sigma * math.sqrt(2.0     )     )
         return 0.5 * (1.0 + torch.erf(A     ))







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def 					__SCREAMING_SNAKE_CASE		(   self			:							List[str]    ,					A			:							List[Any]     )      ->      Tuple:






         """simple docstring"""
         a__  			=							nn.functional.relu(A     )
         a__  			=							torch.square(A     )
         return squared







class 		__UpperCAmelCase(		lowercase__			):





     """simple docstring"""



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






         """simple docstring"""
         a__  			=							super().__getitem__(A     )
         a__			,		a__  			=							content if isinstance(A    ,					A     ) else (content, {})
         return cls(**A     )






SCREAMING_SNAKE_CASE_    :       List[Any]       =							{
    '''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,
}
SCREAMING_SNAKE_CASE_    :       Any       =							ClassInstantier(ACTaCLS)
def 							__lowerCAmelCase			(UpperCAmelCase_		):
    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
SCREAMING_SNAKE_CASE_    :       Optional[int]       =							get_activation('''gelu_python''')
SCREAMING_SNAKE_CASE_    :       Union[str, Any]       =							get_activation('''gelu_new''')
SCREAMING_SNAKE_CASE_    :       str       =							get_activation('''gelu''')
SCREAMING_SNAKE_CASE_    :       Optional[Any]       =							get_activation('''gelu_fast''')
SCREAMING_SNAKE_CASE_    :       Union[str, Any]       =							get_activation('''quick_gelu''')
SCREAMING_SNAKE_CASE_    :       Tuple       =							get_activation('''silu''')
SCREAMING_SNAKE_CASE_    :       Union[str, Any]       =							get_activation('''mish''')







SCREAMING_SNAKE_CASE_    :       Tuple       =							get_activation('''linear''')






