

"""simple docstring"""





import math
from collections import OrderedDict

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

from .utils import logging


_SCREAMING_SNAKE_CASE       :  Union[str, Any]        = logging.get_logger(__name__)

class     __a   (      nn.Module    ):

			"""simple docstring"""

			def __init__(							self     :					List[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    _lowerCAmelCase			(							self     :					Dict						,  lowercase_     :					Tensor						):
								return nn.functional.gelu(lowercase_						,  approximate='''tanh'''						)



class     __a   (      nn.Module    ):

			"""simple docstring"""

			def    _lowerCAmelCase			(							self     :					int						,  lowercase_     :					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(lowercase_						,  3.0						))						))



class     __a   (      nn.Module    ):

			"""simple docstring"""

			def __init__(							self     :					Union[str, Any]						,  lowercase_     :					bool = False						):
								super().__init__()
								if use_gelu_python:
													UpperCamelCase__		:    str         =self._gelu_python
								else:
													UpperCamelCase__		:    List[Any]         =nn.functional.gelu






			def    _lowerCAmelCase			(							self     :					Dict						,  lowercase_     :					Tensor						):
								return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0						)						))






			def    _lowerCAmelCase			(							self     :					Dict						,  lowercase_     :					Tensor						):
								return self.act(lowercase_						)



class     __a   (      nn.Module    ):

			"""simple docstring"""

			def    _lowerCAmelCase			(							self     :					List[Any]						,  lowercase_     :					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     __a   (      nn.Module    ):

			"""simple docstring"""

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



class     __a   (      nn.Module    ):

			"""simple docstring"""

			def __init__(							self     :					Optional[Any]						,  lowercase_     :					float						,  lowercase_     :					float						):
								if min > max:
													raise ValueError(f'''min should be < max (got min: {min}, max: {max})'''						)

								super().__init__()
								UpperCamelCase__		:    Dict         =min
								UpperCamelCase__		:    Any         =max






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



class     __a   (      nn.Module    ):

			"""simple docstring"""

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






			def    _lowerCAmelCase			(							self     :					Union[str, Any]						,  lowercase_     :					Tensor						):
								return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.0_4_4_7_1_5 * torch.pow(lowercase_						,  3						))						))



class     __a   (      nn.Module    ):

			"""simple docstring"""

			def    _lowerCAmelCase			(							self     :					Tuple						,  lowercase_     :					Tensor						):
								return nn.functional.silu(lowercase_						)



class     __a   (      nn.Module    ):

			"""simple docstring"""

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






			def    _lowerCAmelCase			(							self     :					Any						,  lowercase_     :					Tensor						):
								return input * torch.tanh(nn.functional.softplus(lowercase_						)						)






			def    _lowerCAmelCase			(							self     :					Optional[int]						,  lowercase_     :					Tensor						):
								return self.act(lowercase_						)



class     __a   (      nn.Module    ):

			"""simple docstring"""

			def    _lowerCAmelCase			(							self     :					Any						,  lowercase_     :					Tensor						):
								return input



class     __a   (      nn.Module    ):

			"""simple docstring"""

			def    _lowerCAmelCase			(							self     :					Tuple						,  lowercase_     :					List[Any]						,  lowercase_     :					Union[str, Any]=0.7_0_7_1_0_7						,  lowercase_     :					Tuple=0.2_8_2_0_9_5						):
								UpperCamelCase__		:    List[Any]         =(input - mu).div(sigma * math.sqrt(2.0						)						)
								return 0.5 * (1.0 + torch.erf(lowercase_						))



class     __a   (      nn.Module    ):

			"""simple docstring"""

			def    _lowerCAmelCase			(							self     :					Dict						,  lowercase_     :					int						):
								UpperCamelCase__		:    List[Any]         =nn.functional.relu(lowercase_						)
								UpperCamelCase__		:    Optional[int]         =torch.square(lowercase_						)
								return squared



class     __a   (      snake_case__    ):

			"""simple docstring"""

			def __getitem__(							self     :					Tuple						,  lowercase_     :					Dict						):
								UpperCamelCase__		:    str         =super().__getitem__(lowercase_						)
								UpperCamelCase__  ,			UpperCamelCase__		:    str         =content if isinstance(lowercase_						,  lowercase_						) else (content, {})
								return cls(**lowercase_						)



_SCREAMING_SNAKE_CASE       :  Any        = {
    """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,
}
_SCREAMING_SNAKE_CASE       :  List[Any]        = ClassInstantier(ACTaCLS)



def        _lowerCAmelCase      (						UpperCAmelCase     :							int				):


					'''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       :  Union[str, Any]        = get_activation("""gelu_python""")
_SCREAMING_SNAKE_CASE       :  Optional[int]        = get_activation("""gelu_new""")
_SCREAMING_SNAKE_CASE       :  str        = get_activation("""gelu""")
_SCREAMING_SNAKE_CASE       :  Optional[Any]        = get_activation("""gelu_fast""")
_SCREAMING_SNAKE_CASE       :  Any        = get_activation("""quick_gelu""")
_SCREAMING_SNAKE_CASE       :  Union[str, Any]        = get_activation("""silu""")
_SCREAMING_SNAKE_CASE       :  Optional[int]        = get_activation("""mish""")







_SCREAMING_SNAKE_CASE       :  int        = get_activation("""linear""")





"""simple docstring"""





import math as m



def        _lowerCAmelCase      (						UpperCAmelCase     :							Optional[int]      ,							UpperCAmelCase     :							Optional[Any]      ,							UpperCAmelCase     :							Optional[Any]=0.001				):


					'''simple docstring'''






					return (f(a + h				) - f(a - h				)) / (2 * h)



def        _lowerCAmelCase      (						UpperCAmelCase     :							List[str]      ,							UpperCAmelCase     :							Optional[Any]=0      ,							UpperCAmelCase     :							Optional[int]=100      ,							UpperCAmelCase     :							Union[str, Any]=0.0001      ,							UpperCAmelCase     :							Dict=1E-6      ,							UpperCAmelCase     :							Any=False				):


					'''simple docstring'''






					UpperCamelCase__		:    Optional[Any]         =xa  # set the initial guess
					UpperCamelCase__		:    List[Any]         =[a]
					UpperCamelCase__		:    Optional[Any]         =abs(f(UpperCAmelCase				)				)
					UpperCamelCase__		:    List[str]         =lambda UpperCAmelCase				: calc_derivative(UpperCAmelCase      ,							UpperCAmelCase      ,							h=UpperCAmelCase				)  # noqa: E731  Derivative of f(x)
					for _ in range(UpperCAmelCase				):
										if fa(UpperCAmelCase				) == 0:
															raise ValueError('''No converging solution found'''				)
										UpperCamelCase__		:    Any         =a - f(UpperCAmelCase				) / fa(UpperCAmelCase				)  # Calculate the next estimate
										if logsteps:
															steps.append(UpperCAmelCase				)
										if error < maxerror:
															break
					else:
										raise ValueError('''Iteration limit reached, no converging solution found'''				)
					if logsteps:
										# If logstep is true, then log intermediate steps
										return a, error, steps
					return a, error


if __name__ == "__main__":
				from matplotlib import pyplot as plt

				_SCREAMING_SNAKE_CASE       :  str        = lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
				_SCREAMING_SNAKE_CASE       ,       _SCREAMING_SNAKE_CASE       ,       _SCREAMING_SNAKE_CASE       :  Any        = newton_raphson(
				    f, xa=1_0, maxiter=1_0_0_0, step=1e-6, logsteps=True
				)
				plt.plot([abs(f(x)) for x in steps])
				plt.xlabel("""step""")
				plt.ylabel("""error""")
				plt.show()
				print(F'''solution = {{{solution:f}}}, error = {{{error:f}}}''')


