
import math
from collections import OrderedDict

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

from .utils import logging


snake_case_      :					Optional[int]				      =     logging.get_logger(__name__)





class 		__lowerCamelCase     (   nn.Module		):



							def __init__(     self							)						->     Optional[int]:






								"""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     A__ (     self	,				__snake_case							)						->     Dict:






								"""simple docstring"""



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





class 		__lowerCamelCase     (   nn.Module		):



							def     A__ (     self	,				__snake_case							)						->     Tuple:






								"""simple docstring"""



								return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi							) * (input + 0.04_47_15 * torch.pow(_SCREAMING_SNAKE_CASE	,				3.0							))							))





class 		__lowerCamelCase     (   nn.Module		):



							def __init__(     self	,				__snake_case = False							)						->     str:






								"""simple docstring"""



								super().__init__()
								if use_gelu_python:
									UpperCAmelCase:					List[str] 						=     self._gelu_python
								else:
									UpperCAmelCase:					List[Any] 						=     nn.functional.gelu



							def     A__ (     self	,				__snake_case							)						->     List[str]:






								"""simple docstring"""



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




							def     A__ (     self	,				__snake_case							)						->     Tuple:






								"""simple docstring"""



								return self.act(_SCREAMING_SNAKE_CASE							)





class 		__lowerCamelCase     (   nn.Module		):



							def     A__ (     self	,				__snake_case							)						->     Optional[int]:






								"""simple docstring"""



								return 0.5 * input * (1.0 + torch.tanh(input * 0.79_78_84_56_08 * (1.0 + 0.04_47_15 * input * input)							))





class 		__lowerCamelCase     (   nn.Module		):



							def     A__ (     self	,				__snake_case							)						->     List[Any]:






								"""simple docstring"""



								return input * torch.sigmoid(1.7_02 * input							)





class 		__lowerCamelCase     (   nn.Module		):



							def __init__(     self	,				__snake_case	,				__snake_case							)						->     Any:






								"""simple docstring"""



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

								super().__init__()
								UpperCAmelCase:					Optional[int] 						=     min
								UpperCAmelCase:					Optional[Any] 						=     max




							def     A__ (     self	,				__snake_case							)						->     List[Any]:






								"""simple docstring"""



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





class 		__lowerCamelCase     (   nn.Module		):



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






								"""simple docstring"""



								super().__init__()
								UpperCAmelCase:					Union[str, Any] 						=     math.sqrt(2 / math.pi							)




							def     A__ (     self	,				__snake_case							)						->     Tuple:






								"""simple docstring"""



								return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.04_47_15 * torch.pow(_SCREAMING_SNAKE_CASE	,				3							))							))





class 		__lowerCamelCase     (   nn.Module		):



							def     A__ (     self	,				__snake_case							)						->     Optional[int]:






								"""simple docstring"""



								return nn.functional.silu(_SCREAMING_SNAKE_CASE							)





class 		__lowerCamelCase     (   nn.Module		):



							def __init__(     self							)						->     Tuple:






								"""simple docstring"""



								super().__init__()
								if version.parse(torch.__version__							) < version.parse("1.9.0"							):
									UpperCAmelCase:					int 						=     self._mish_python
								else:
									UpperCAmelCase:					Union[str, Any] 						=     nn.functional.mish



							def     A__ (     self	,				__snake_case							)						->     List[str]:






								"""simple docstring"""



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




							def     A__ (     self	,				__snake_case							)						->     List[Any]:






								"""simple docstring"""



								return self.act(_SCREAMING_SNAKE_CASE							)





class 		__lowerCamelCase     (   nn.Module		):



							def     A__ (     self	,				__snake_case							)						->     Optional[Any]:






								"""simple docstring"""



								return input





class 		__lowerCamelCase     (   nn.Module		):



							def     A__ (     self	,				__snake_case	,				__snake_case=0.70_71_07	,				__snake_case=0.28_20_95							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase:					List[str] 						=     (input - mu).div(sigma * math.sqrt(2.0							)							)
								return 0.5 * (1.0 + torch.erf(_SCREAMING_SNAKE_CASE							))





class 		__lowerCamelCase     (   nn.Module		):



							def     A__ (     self	,				__snake_case							)						->     List[Any]:






								"""simple docstring"""



								UpperCAmelCase:					Dict 						=     nn.functional.relu(_SCREAMING_SNAKE_CASE							)
								UpperCAmelCase:					Any 						=     torch.square(_SCREAMING_SNAKE_CASE							)
								return squared





class 		__lowerCamelCase     (   _SCREAMING_SNAKE_CASE		):



							def __getitem__(     self	,				__snake_case							)						->     Dict:






								"""simple docstring"""



								UpperCAmelCase:					Any 						=     super().__getitem__(_SCREAMING_SNAKE_CASE							)
								UpperCAmelCase						,   UpperCAmelCase:					Dict 						=     content if isinstance(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE							) else (content, {})
								return cls(**_SCREAMING_SNAKE_CASE							)


snake_case_      :					Dict				      =     {
    '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,
}
snake_case_      :					Tuple				      =     ClassInstantier(ACTaCLS)



def      __UpperCAmelCase    (  snake_case_							:		Tuple				):



	'''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
snake_case_      :					List[str]				      =     get_activation('gelu_python')
snake_case_      :					Dict				      =     get_activation('gelu_new')
snake_case_      :					Union[str, Any]				      =     get_activation('gelu')
snake_case_      :					Optional[Any]				      =     get_activation('gelu_fast')
snake_case_      :					Tuple				      =     get_activation('quick_gelu')
snake_case_      :					List[str]				      =     get_activation('silu')
snake_case_      :					int				      =     get_activation('mish')


snake_case_      :					Union[str, Any]				      =     get_activation('linear')




import enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional

from .logging import get_logger


_A							     =       set()
_A							     =       get_logger(__name__)

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Optional[str] = None	)     ->	Optional[int]:



	"""simple docstring"""






	def decorator(UpperCamelCase		:      Callable	):
		global _emitted_deprecation_warnings

		if inspect.isclass(UpperCamelCase	):
			a_     			=   deprecated_class_or_function.__init__
			a_     			=   deprecated_class_or_function.__name__
		else:
			a_     			=   deprecated_class_or_function
			a_     			=   deprecated_function.__name__
			# Support deprecating __init__ class method: class name instead
			a_     			=   name if name != """__init__""" else deprecated_function.__qualname__.split("""."""	)[-2]

		a_     			=   (
		    F"""{name} is deprecated and will be removed in the next major version of datasets.""" + F""" {help_message}"""
		    if help_message
		    else """"""
		)

		@wraps(UpperCamelCase	)
		def wrapper(*UpperCamelCase		:      List[str]     ,					**UpperCamelCase		:      Union[str, Any]	):
			a_     			=   hash(UpperCamelCase	)
			if func_hash not in _emitted_deprecation_warnings:
				warnings.warn(UpperCamelCase     ,					category=UpperCamelCase     ,					stacklevel=2	)
				_emitted_deprecation_warnings.add(UpperCamelCase	)
			return deprecated_function(*UpperCamelCase     ,					**UpperCamelCase	)

		a_     			=   """deprecated"""

		if inspect.isclass(UpperCamelCase	):
			a_     			=   wrapper
			return deprecated_class_or_function
		else:
			return wrapper

	return decorator

class 			lowerCamelCase_					(    enum.EnumMeta    ):





							def __getattribute__(							cls			,       _SCREAMING_SNAKE_CASE    ):
								a_     			=   super().__getattribute__(_SCREAMING_SNAKE_CASE    )
								if isinstance(_SCREAMING_SNAKE_CASE			,       enum.Enum    ) and obj._on_access:
									obj._on_access()
								return obj





							def __getitem__(							cls			,       _SCREAMING_SNAKE_CASE    ):
								a_     			=   super().__getitem__(_SCREAMING_SNAKE_CASE    )
								if member._on_access:
									member._on_access()
								return member





							def __call__(							cls			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE=None			,       *, _SCREAMING_SNAKE_CASE=None			,       _SCREAMING_SNAKE_CASE=None			,       _SCREAMING_SNAKE_CASE=None			,       _SCREAMING_SNAKE_CASE=1    ):
								a_     			=   super().__call__(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       module=_SCREAMING_SNAKE_CASE			,       qualname=_SCREAMING_SNAKE_CASE			,       type=_SCREAMING_SNAKE_CASE			,       start=_SCREAMING_SNAKE_CASE    )
								if isinstance(_SCREAMING_SNAKE_CASE			,       enum.Enum    ) and obj._on_access:
									obj._on_access()
								return obj







class 			lowerCamelCase_					(    enum.Enum   ,		metaclass=_SCREAMING_SNAKE_CASE    ):





							def __new__(							cls			,       _SCREAMING_SNAKE_CASE    ):
								a_     			=   object.__new__(cls    )
								a_     			=   value
								a_     			=   member.deprecate
								return member





							@property
							def   __magic_name__      (							self    ):
								return ""





							def   __magic_name__      (							self    ):
								a_     			=   f""" {self.help_message}""" if self.help_message else """"""
								warnings.warn(
								    f"""'{self.__objclass__.__name__}' is deprecated and will be removed in the next major version of datasets."""
								    + help_message			,       _SCREAMING_SNAKE_CASE			,       stacklevel=3			,       )