



'''simple docstring'''




import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer






class 						_UpperCamelCase  (							nn.Linear   ):


			'''simple docstring'''

			def __init__(							self					:			List[str]							,					lowerCAmelCase__					:			int							,					lowerCAmelCase__					:			int							,					lowerCAmelCase__					:			bool = True							,					lowerCAmelCase__					:			str = "constant"							,					lowerCAmelCase__					:			float = 0.0							,					lowerCAmelCase__					:			str = "topK"							,					):





					"""simple docstring"""






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

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

			def UpperCamelCase__ (							self					:			List[Any]						):





					"""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 UpperCamelCase__ (							self					:			List[str]							,					lowerCAmelCase__					:			torch.tensor							,					lowerCAmelCase__					:			float						):





					"""simple docstring"""






					if self.pruning_method == "topK":
							__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		TopKBinarizer.apply(self.mask_scores							,					lowerCAmelCase__						)
					elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
							__SCREAMING_SNAKE_CASE	:		int   	=		"""sigmoied""" in self.pruning_method
							__SCREAMING_SNAKE_CASE	:		List[Any]   	=		ThresholdBinarizer.apply(self.mask_scores							,					lowerCAmelCase__							,					lowerCAmelCase__						)
					elif self.pruning_method == "magnitude":
							__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		MagnitudeBinarizer.apply(self.weight							,					lowerCAmelCase__						)
					elif self.pruning_method == "l0":
							__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		-0.1, 1.1, 2 / 3
							if self.training:
									__SCREAMING_SNAKE_CASE	:		List[str]   	=		torch.zeros_like(self.mask_scores						).uniform_().clamp(0.00_01							,					0.99_99						)
									__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b						)
							else:
									__SCREAMING_SNAKE_CASE	:		Tuple   	=		torch.sigmoid(self.mask_scores						)
							__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		s * (r - l) + l
							__SCREAMING_SNAKE_CASE	:		Dict   	=		s_bar.clamp(min=0.0							,					max=1.0						)
					# Mask weights with computed mask
					__SCREAMING_SNAKE_CASE	:		str   	=		mask * self.weight
					# Compute output (linear layer) with masked weights
					return nn.functional.linear(lowerCAmelCase__							,					lowerCAmelCase__							,					self.bias						)




'''simple docstring'''




UpperCamelCase__	:     List[Any]    =			10_00


def     lowerCAmelCase_       (     _lowerCamelCase:       float ):
		return x * x


def     lowerCAmelCase_       (     _lowerCamelCase:       Any	,				_lowerCamelCase:       float	,				_lowerCamelCase:       float	,				_lowerCamelCase:       int = 4 ):
		assert callable(
		    _lowerCamelCase ), F"the function(object) passed should be callable your input : {function}"
		assert isinstance(_lowerCamelCase	,				(float, int) ), F"a should be float or integer your input : {a}"
		assert isinstance(function(_lowerCamelCase )	,				(float, int) ), (
		    "the function should return integer or float return type of your function, "
		    F"{type(_lowerCamelCase )}"
		)
		assert isinstance(_lowerCamelCase	,				(float, int) ), F"b should be float or integer your input : {b}"
		assert (
		    isinstance(_lowerCamelCase	,				_lowerCamelCase ) and precision > 0
		), F"precision should be positive integer your input : {precision}"

		# just applying the formula of simpson for approximate integration written in
		# mentioned article in first comment of this file and above this function

		__SCREAMING_SNAKE_CASE	:		List[Any]   	=		(b - a) / N_STEPS
		__SCREAMING_SNAKE_CASE	:		List[Any]   	=		function(_lowerCamelCase ) + function(_lowerCamelCase )

		for i in range(1	,				_lowerCamelCase ):
				__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		a + h * i
				result += function(_lowerCamelCase ) * (4 if i % 2 else 2)

		result *= h / 3
		return round(_lowerCamelCase	,				_lowerCamelCase )


if __name__ == "__main__":
						import doctest

						doctest.testmod()