
"""simple docstring"""
import math
from collections import OrderedDict

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

from .utils import logging


lowercase		:						Optional[Any]			=					logging.get_logger(__name__)




class    _snake_case  (						nn.Module       ):






							def __init__(					self						:			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 							__lowercase  (					self						:			List[Any]						, snake_case__						:			List[Any]    ):






												"""simple docstring"""



												return nn.functional.gelu(__UpperCamelCase						, approximate="""tanh"""    )






class    _snake_case  (						nn.Module       ):






							def 							__lowercase  (					self						:			List[Any]						, snake_case__						:			List[Any]    ):






												"""simple docstring"""



												return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi    ) * (input + 0.044_715 * torch.pow(__UpperCamelCase						, 3.0    ))    ))






class    _snake_case  (						nn.Module       ):






							def __init__(					self						:			Union[str, Any]						, snake_case__						:			Optional[int] = False    ):






												"""simple docstring"""



												super().__init__()
												if use_gelu_python:
																	__a:						str		    =					self._gelu_python
												else:
																	__a:						Optional[int]		    =					nn.functional.gelu






							def 							__lowercase  (					self						:			int						, snake_case__						:			int    ):






												"""simple docstring"""



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






							def 							__lowercase  (					self						:			Optional[int]						, snake_case__						:			Any    ):






												"""simple docstring"""



												return self.act(__UpperCamelCase    )






class    _snake_case  (						nn.Module       ):






							def 							__lowercase  (					self						:			Tuple						, snake_case__						:			Dict    ):






												"""simple docstring"""



												return 0.5 * input * (1.0 + torch.tanh(input * 0.7_978_845_608 * (1.0 + 0.044_715 * input * input)    ))






class    _snake_case  (						nn.Module       ):






							def 							__lowercase  (					self						:			Any						, snake_case__						:			Optional[Any]    ):






												"""simple docstring"""



												return input * torch.sigmoid(1.702 * input    )






class    _snake_case  (						nn.Module       ):






							def __init__(					self						:			List[str]						, snake_case__						:			Tuple						, snake_case__						:			Optional[int]    ):






												"""simple docstring"""



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

												super().__init__()
												__a:						Dict		    =					min
												__a:						Union[str, Any]		    =					max






							def 							__lowercase  (					self						:			Tuple						, snake_case__						:			List[str]    ):






												"""simple docstring"""



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






class    _snake_case  (						nn.Module       ):






							def __init__(					self						:			Any    ):






												"""simple docstring"""



												super().__init__()
												__a:						Tuple		    =					math.sqrt(2 / math.pi    )






							def 							__lowercase  (					self						:			Any						, snake_case__						:			Union[str, Any]    ):






												"""simple docstring"""



												return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.044_715 * torch.pow(__UpperCamelCase						, 3    ))    ))






class    _snake_case  (						nn.Module       ):






							def 							__lowercase  (					self						:			List[str]						, snake_case__						:			Any    ):






												"""simple docstring"""



												return nn.functional.silu(__UpperCamelCase    )






class    _snake_case  (						nn.Module       ):






							def __init__(					self						:			Tuple    ):






												"""simple docstring"""



												super().__init__()
												if version.parse(torch.__version__    ) < version.parse("""1.9.0"""    ):
																	__a:						List[Any]		    =					self._mish_python
												else:
																	__a:						List[Any]		    =					nn.functional.mish






							def 							__lowercase  (					self						:			Any						, snake_case__						:			Optional[Any]    ):






												"""simple docstring"""



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






							def 							__lowercase  (					self						:			List[Any]						, snake_case__						:			Optional[int]    ):






												"""simple docstring"""



												return self.act(__UpperCamelCase    )






class    _snake_case  (						nn.Module       ):






							def 							__lowercase  (					self						:			Any						, snake_case__						:			Any    ):






												"""simple docstring"""



												return input






class    _snake_case  (						nn.Module       ):






							def 							__lowercase  (					self						:			str						, snake_case__						:			Dict						, snake_case__						:			Tuple=0.707_107						, snake_case__						:			Dict=0.282_095    ):






												"""simple docstring"""



												__a:						Any		    =					(input - mu).div(sigma * math.sqrt(2.0    )    )
												return 0.5 * (1.0 + torch.erf(__UpperCamelCase    ))






class    _snake_case  (						nn.Module       ):






							def 							__lowercase  (					self						:			Optional[Any]						, snake_case__						:			Tuple    ):






												"""simple docstring"""



												__a:						Union[str, Any]		    =					nn.functional.relu(__UpperCamelCase    )
												__a:						Any		    =					torch.square(__UpperCamelCase    )
												return squared






class    _snake_case  (						UpperCamelCase_       ):






							def __getitem__(					self						:			Any						, snake_case__						:			Union[str, Any]    ):






												"""simple docstring"""



												__a:						int		    =					super().__getitem__(__UpperCamelCase    )
												__a:						Tuple		    =					content if isinstance(__UpperCamelCase						, __UpperCamelCase    ) else (content, {})
												return cls(**__UpperCamelCase    )






lowercase		:						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,
}
lowercase		:						List[str]			=					ClassInstantier(ACTaCLS)


def     lowerCamelCase_    (	lowerCamelCase_ )     ->			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
lowercase		:						Optional[Any]			=					get_activation('gelu_python')
lowercase		:						List[str]			=					get_activation('gelu_new')
lowercase		:						Any			=					get_activation('gelu')
lowercase		:						Tuple			=					get_activation('gelu_fast')
lowercase		:						Dict			=					get_activation('quick_gelu')
lowercase		:						Union[str, Any]			=					get_activation('silu')
lowercase		:						Dict			=					get_activation('mish')




lowercase		:						List[Any]			=					get_activation('linear')


"""simple docstring"""
lowercase		:						str =					{
    '0000': '0',
    '0001': '1',
    '0010': '2',
    '0011': '3',
    '0100': '4',
    '0101': '5',
    '0110': '6',
    '0111': '7',
    '1000': '8',
    '1001': '9',
    '1010': 'a',
    '1011': 'b',
    '1100': 'c',
    '1101': 'd',
    '1110': 'e',
    '1111': 'f',
}


def     lowerCamelCase_    (	lowerCamelCase_ )     ->			str:


					'''simple docstring'''
					__a:						Optional[int]		    =					str(lowerCamelCase_ ).strip()

					# Exceptions
					if not binary_str:
										raise ValueError("""Empty string was passed to the function""" )
					__a:						List[Any]		    =					binary_str[0] == """-"""
					__a:						List[str]		    =					binary_str[1:] if is_negative else binary_str
					if not all(char in """01""" for char in binary_str ):
										raise ValueError("""Non-binary value was passed to the function""" )

					__a:						Any		    =					(
					    """0""" * (4 * (divmod(len(lowerCamelCase_ )	,				4 )[0] + 1) - len(lowerCamelCase_ )) + binary_str
					)

					__a:						int		    =					[]
					for x in range(0	,				len(lowerCamelCase_ )	,				4 ):
										hexadecimal.append(BITS_TO_HEX[binary_str[x : x + 4]] )
					__a:						Tuple		    =					"""0x""" + """""".join(lowerCamelCase_ )

					return "-" + hexadecimal_str if is_negative else hexadecimal_str


if __name__ == "__main__":
			from doctest import testmod

			testmod()
