
import math
from collections import OrderedDict

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

from .utils import logging


__SCREAMING_SNAKE_CASE     :				int      					=       logging.get_logger(__name__)


class 						__magic_name__				(  nn.Module      ):





		def __init__(							self      :		Optional[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  _A							(							self      :		Optional[int]  ,					lowerCamelCase__      :		Tensor			):
							return nn.functional.gelu(lowerCamelCase__  ,					approximate='''tanh'''			)



class 						__magic_name__				(  nn.Module      ):





		def  _A							(							self      :		Dict  ,					lowerCamelCase__      :		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(lowerCamelCase__  ,					3.0			))			))



class 						__magic_name__				(  nn.Module      ):





		def __init__(							self      :		Dict  ,					lowerCamelCase__      :		bool = False			):
							super().__init__()
							if use_gelu_python:
												lowerCAmelCase						:   Any							=       self._gelu_python
							else:
												lowerCAmelCase						:   int							=       nn.functional.gelu





		def  _A							(							self      :		Optional[int]  ,					lowerCamelCase__      :		Tensor			):
							return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0			)			))





		def  _A							(							self      :		Union[str, Any]  ,					lowerCamelCase__      :		Tensor			):
							return self.act(lowerCamelCase__			)



class 						__magic_name__				(  nn.Module      ):





		def  _A							(							self      :		Tuple  ,					lowerCamelCase__      :		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 						__magic_name__				(  nn.Module      ):





		def  _A							(							self      :		Tuple  ,					lowerCamelCase__      :		Tensor			):
							return input * torch.sigmoid(1.7_0_2 * input			)



class 						__magic_name__				(  nn.Module      ):





		def __init__(							self      :		Tuple  ,					lowerCamelCase__      :		float  ,					lowerCamelCase__      :		float			):
							if min > max:
												raise ValueError(f'''min should be < max (got min: {min}, max: {max})'''			)

							super().__init__()
							lowerCAmelCase						:   int							=       min
							lowerCAmelCase						:   Union[str, Any]							=       max





		def  _A							(							self      :		Tuple  ,					lowerCamelCase__      :		Tensor			):
							return torch.clip(gelu(lowerCamelCase__			)  ,					self.min  ,					self.max			)



class 						__magic_name__				(  nn.Module      ):





		def __init__(							self      :		Union[str, Any]			):
							super().__init__()
							lowerCAmelCase						:   Tuple							=       math.sqrt(2 / math.pi			)





		def  _A							(							self      :		Optional[Any]  ,					lowerCamelCase__      :		Tensor			):
							return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.0_4_4_7_1_5 * torch.pow(lowerCamelCase__  ,					3			))			))



class 						__magic_name__				(  nn.Module      ):





		def  _A							(							self      :		Any  ,					lowerCamelCase__      :		Tensor			):
							return nn.functional.silu(lowerCamelCase__			)



class 						__magic_name__				(  nn.Module      ):





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





		def  _A							(							self      :		int  ,					lowerCamelCase__      :		Tensor			):
							return input * torch.tanh(nn.functional.softplus(lowerCamelCase__			)			)





		def  _A							(							self      :		List[Any]  ,					lowerCamelCase__      :		Tensor			):
							return self.act(lowerCamelCase__			)



class 						__magic_name__				(  nn.Module      ):





		def  _A							(							self      :		str  ,					lowerCamelCase__      :		Tensor			):
							return input



class 						__magic_name__				(  nn.Module      ):





		def  _A							(							self      :		Union[str, Any]  ,					lowerCamelCase__      :		int  ,					lowerCamelCase__      :		List[str]=0.7_0_7_1_0_7  ,					lowerCamelCase__      :		List[str]=0.2_8_2_0_9_5			):
							lowerCAmelCase						:   Optional[int]							=       (input - mu).div(sigma * math.sqrt(2.0			)			)
							return 0.5 * (1.0 + torch.erf(lowerCamelCase__			))



class 						__magic_name__				(  nn.Module      ):





		def  _A							(							self      :		Dict  ,					lowerCamelCase__      :		Dict			):
							lowerCAmelCase						:   int							=       nn.functional.relu(lowerCamelCase__			)
							lowerCAmelCase						:   str							=       torch.square(lowerCamelCase__			)
							return squared



class 						__magic_name__				(  snake_case      ):





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



__SCREAMING_SNAKE_CASE     :				Dict      					=       {
    '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     :				Optional[int]      					=       ClassInstantier(ACTaCLS)
def       UpperCAmelCase__							(    __magic_name__  :       Union[str, Any]					):


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


def       UpperCAmelCase__							(    __magic_name__  :       Union[str, Any]							,					__magic_name__  :       int					):


					'''simple docstring'''

					sequence.append(__magic_name__					)

					lowerCAmelCase						:   Tuple							=       0
					while sequence[index] != target:
										index += 1

					sequence.pop()

					if index == len(__magic_name__					):
										return None

					return index


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE     :				Dict             =       input('Enter numbers separated by comma:\n').strip()
		__SCREAMING_SNAKE_CASE     :				int             =       [int(item) for item in user_input.split(',')]

		__SCREAMING_SNAKE_CASE     :				str             =       input('Enter a single number to be found in the list:\n')
		__SCREAMING_SNAKE_CASE     :				Tuple             =       int(target_input)
		__SCREAMING_SNAKE_CASE     :				Union[str, Any]             =       sentinel_linear_search(sequence, target)
		if result is not None:
				print(f"""{target} found at positions: {result}""")
		else:
				print('Not found')
