




import math

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer

class     lowercase (      nn.Linear):







				def __init__(							self				:   List[Any] ,       _lowerCamelCase				:   Optional[int] ,       _lowerCamelCase				:   Tuple ,       _lowerCamelCase				:   Optional[int] = True ,       _lowerCamelCase				:   Optional[Any] = "constant" ,       _lowerCamelCase				:   List[str] = 0.0 ,       _lowerCamelCase				:   Optional[int] = "topK" ,       ):



					"""simple docstring"""

					super(__A ,       self    ).__init__(in_features=__A ,       out_features=__A ,       bias=__A    )
					assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
					A_					:			List[str]						=						pruning_method

					if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
						A_					:			List[Any]						=						mask_scale
						A_					:			Optional[Any]						=						mask_init
						A_					:			str						=						nn.Parameter(torch.empty(self.weight.size()    )    )
						self.init_mask()







				def    a_					(							self				:   Optional[int]    ):



					"""simple docstring"""

					if self.mask_init == "constant":
						init.constant_(self.mask_scores ,       val=self.mask_scale    )
					elif self.mask_init == "uniform":
						init.uniform_(self.mask_scores ,       a=-self.mask_scale ,       b=self.mask_scale    )
					elif self.mask_init == "kaiming":
						init.kaiming_uniform_(self.mask_scores ,       a=math.sqrt(5    )    )







				def    a_					(							self				:   Any ,       _lowerCamelCase				:   int ,       _lowerCamelCase				:   Optional[int]    ):



					"""simple docstring"""

					if self.pruning_method == "topK":
						A_					:			Any						=						TopKBinarizer.apply(self.mask_scores ,       __A    )
					elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
						A_					:			Optional[int]						=						"sigmoied" in self.pruning_method
						A_					:			Dict						=						ThresholdBinarizer.apply(self.mask_scores ,       __A ,       __A    )
					elif self.pruning_method == "magnitude":
						A_					:			List[Any]						=						MagnitudeBinarizer.apply(self.weight ,       __A    )
					elif self.pruning_method == "l0":
						A_					:			List[Any]						=						-0.1, 1.1, 2 / 3
						if self.training:
							A_					:			Optional[int]						=						torch.zeros_like(self.mask_scores    ).uniform_().clamp(0.0001 ,       0.9999    )
							A_					:			List[str]						=						torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b    )
						else:
							A_					:			Dict						=						torch.sigmoid(self.mask_scores    )
						A_					:			List[Any]						=						s * (r - l) + l
						A_					:			Optional[Any]						=						s_bar.clamp(min=0.0 ,       max=1.0    )
					# Mask weights with computed mask
					A_					:			Optional[Any]						=						mask * self.weight
					# Compute output (linear layer) with masked weights
					return nn.functional.linear(__A ,       __A ,       self.bias    )











"""simple docstring"""

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

from .logging import get_logger


_lowerCamelCase					:     Optional[int]				       =    set()
_lowerCamelCase					:     Union[str, Any]				       =    get_logger(__name__)

def 						lowercase_ (						_UpperCAmelCase = None		):




	"""simple docstring"""






	def decorator(_UpperCAmelCase		):
		global _emitted_deprecation_warnings

		if inspect.isclass(_UpperCAmelCase		):
			A_					:			Union[str, Any]						=						deprecated_class_or_function.__init__
			A_					:			int						=						deprecated_class_or_function.__name__
		else:
			A_					:			Union[str, Any]						=						deprecated_class_or_function
			A_					:			List[Any]						=						deprecated_function.__name__
			# Support deprecating __init__ class method: class name instead
			A_					:			List[Any]						=						name if name != '''__init__''' else deprecated_function.__qualname__.split('''.'''		)[-2]

		A_					:			str						=						(
		    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					, **_UpperCAmelCase		):
			A_					:			Any						=						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_					:			Any						=						'''deprecated'''

		if inspect.isclass(_UpperCAmelCase		):
			A_					:			List[Any]						=						wrapper
			return deprecated_class_or_function
		else:
			return wrapper

	return decorator

class     lowercase (      enum.EnumMeta):







				def __getattribute__(							cls				:   Any ,       _lowerCamelCase				:   List[Any]    ):



					"""simple docstring"""

					A_					:			str						=						super().__getattribute__(_lowerCamelCase    )
					if isinstance(_lowerCamelCase ,       enum.Enum    ) and obj._on_access:
						obj._on_access()
					return obj







				def __getitem__(							cls				:   Any ,       _lowerCamelCase				:   Union[str, Any]    ):



					"""simple docstring"""

					A_					:			Optional[Any]						=						super().__getitem__(_lowerCamelCase    )
					if member._on_access:
						member._on_access()
					return member







				def __call__(							cls				:   str ,       _lowerCamelCase				:   str ,       _lowerCamelCase				:   List[Any]=None ,       *, _lowerCamelCase				:   int=None ,       _lowerCamelCase				:   str=None ,       _lowerCamelCase				:   Dict=None ,       _lowerCamelCase				:   Tuple=1    ):



					"""simple docstring"""

					A_					:			Dict						=						super().__call__(_lowerCamelCase ,       _lowerCamelCase ,       module=_lowerCamelCase ,       qualname=_lowerCamelCase ,       type=_lowerCamelCase ,       start=_lowerCamelCase    )
					if isinstance(_lowerCamelCase ,       enum.Enum    ) and obj._on_access:
						obj._on_access()
					return obj

class     lowercase (      enum.Enum       ,		metaclass=__UpperCAmelCase):







				def __new__(							cls				:   Optional[Any] ,       _lowerCamelCase				:   int    ):



					"""simple docstring"""

					A_					:			Tuple						=						object.__new__(cls    )
					A_					:			Union[str, Any]						=						value
					A_					:			Union[str, Any]						=						member.deprecate
					return member







				@property
				def    a_					(							self				:   Tuple    ):



					"""simple docstring"""

					return ""







				def    a_					(							self				:   Any    ):



					"""simple docstring"""

					A_					:			Optional[int]						=						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 ,       _lowerCamelCase ,       stacklevel=3 ,       )





