






from manim import *

class        _SCREAMING_SNAKE_CASE			(     UpperCamelCase__ ):





	def 		_A					(						self							:   List[Any]      ):
		UpperCamelCase	:Union[str, Any]		=      Rectangle(height=0.5					,    width=0.5      )
		UpperCamelCase	:Tuple		=      Rectangle(height=0.46					,    width=0.46      ).set_stroke(width=0      )
		UpperCamelCase	:Tuple		=      Rectangle(height=0.25					,    width=0.25      )

		UpperCamelCase	:str		=      [mem.copy() for i in range(6      )]
		UpperCamelCase	:List[str]		=      [mem.copy() for i in range(6      )]
		UpperCamelCase	:Tuple		=      VGroup(*_UpperCAmelCase      ).arrange(_UpperCAmelCase					,    buff=0      )
		UpperCamelCase	:Dict		=      VGroup(*_UpperCAmelCase      ).arrange(_UpperCAmelCase					,    buff=0      )
		UpperCamelCase	:Dict		=      VGroup(_UpperCAmelCase					,    _UpperCAmelCase      ).arrange(_UpperCAmelCase					,    buff=0      )
		UpperCamelCase	:Dict		=      Text("""CPU"""					,    font_size=24      )
		UpperCamelCase	:Any		=      Group(_UpperCAmelCase					,    _UpperCAmelCase      ).arrange(_UpperCAmelCase					,    buff=0.5					,    aligned_edge=_UpperCAmelCase      )
		cpu.move_to([-2.5, -0.5, 0]      )
		self.add(_UpperCAmelCase      )

		UpperCamelCase	:str		=      [mem.copy() for i in range(4      )]
		UpperCamelCase	:List[Any]		=      VGroup(*_UpperCAmelCase      ).arrange(_UpperCAmelCase					,    buff=0      )
		UpperCamelCase	:str		=      Text("""GPU"""					,    font_size=24      )
		UpperCamelCase	:Optional[int]		=      Group(_UpperCAmelCase					,    _UpperCAmelCase      ).arrange(_UpperCAmelCase					,    buff=0.5					,    aligned_edge=_UpperCAmelCase      )
		gpu.move_to([-1, -1, 0]      )
		self.add(_UpperCAmelCase      )

		UpperCamelCase	:Dict		=      [mem.copy() for i in range(6      )]
		UpperCamelCase	:Optional[Any]		=      VGroup(*_UpperCAmelCase      ).arrange(_UpperCAmelCase					,    buff=0      )

		UpperCamelCase	:Union[str, Any]		=      Text("""Model"""					,    font_size=24      )
		UpperCamelCase	:Union[str, Any]		=      Group(_UpperCAmelCase					,    _UpperCAmelCase      ).arrange(_UpperCAmelCase					,    buff=0.5					,    aligned_edge=_UpperCAmelCase      )
		model.move_to([3, -1.0, 0]      )
		self.add(_UpperCAmelCase      )

		UpperCamelCase	:str		=      []
		UpperCamelCase	:Any		=      []

		for i, rect in enumerate(_UpperCAmelCase      ):
			rect.set_stroke(_UpperCAmelCase      )

			UpperCamelCase	:str		=      Rectangle(height=0.46 / 4					,    width=0.46 / 3      ).set_stroke(width=0.0      ).set_fill(_UpperCAmelCase					,    opacity=0.7      )

			if i == 0:
				cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT      )					,    buff=0.02					,    direction=_UpperCAmelCase      )
				cpu_target.set_x(cpu_target.get_x() + 0.1      )
			elif i == 3:
				cpu_target.next_to(model_cpu_arr[0]					,    direction=_UpperCAmelCase					,    buff=0.0      )
			else:
				cpu_target.next_to(model_cpu_arr[i - 1]					,    direction=_UpperCAmelCase					,    buff=0.0      )
			self.add(_UpperCAmelCase      )
			model_cpu_arr.append(_UpperCAmelCase      )

		self.add(*_UpperCAmelCase					,    *_UpperCAmelCase      )

		UpperCamelCase	:List[str]		=      [meta_mem.copy() for i in range(6      )]
		UpperCamelCase	:Union[str, Any]		=      [meta_mem.copy() for i in range(6      )]
		UpperCamelCase	:Union[str, Any]		=      VGroup(*_UpperCAmelCase      ).arrange(_UpperCAmelCase					,    buff=0      )
		UpperCamelCase	:List[Any]		=      VGroup(*_UpperCAmelCase      ).arrange(_UpperCAmelCase					,    buff=0      )
		UpperCamelCase	:int		=      VGroup(_UpperCAmelCase					,    _UpperCAmelCase      ).arrange(_UpperCAmelCase					,    buff=0      )
		UpperCamelCase	:Optional[int]		=      Text("""Disk"""					,    font_size=24      )
		UpperCamelCase	:Dict		=      Group(_UpperCAmelCase					,    _UpperCAmelCase      ).arrange(_UpperCAmelCase					,    buff=0.5					,    aligned_edge=_UpperCAmelCase      )
		disk.move_to([-4.0, -1.25, 0]      )
		self.add(_UpperCAmelCase					,    _UpperCAmelCase      )

		UpperCamelCase	:Union[str, Any]		=      []

		for i in range(6      ):
			UpperCamelCase	:str		=      fill.copy().set_fill(_UpperCAmelCase					,    opacity=0.8      )
			target.move_to(disk_left_col_base[i]      ).scale(0.5      )
			cpu_disk_arr.append(_UpperCAmelCase      )

		self.add(*_UpperCAmelCase      )

		UpperCamelCase	:Optional[int]		=      Square(side_length=2.2      )
		key.move_to([-5, 2, 0]      )

		UpperCamelCase	:int		=      MarkupText(
		    F"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"""					,    font_size=18					,    )

		key_text.move_to([-5, 2.4, 0]      )

		self.add(_UpperCAmelCase					,    _UpperCAmelCase      )

		UpperCamelCase	:Dict		=      MarkupText(
		    F"""<span fgcolor='{BLUE}'>●</span> Checkpoint"""					,    font_size=18					,    )

		blue_text.next_to(_UpperCAmelCase					,    DOWN * 2.4					,    aligned_edge=key_text.get_left()      )
		self.add(_UpperCAmelCase      )

		UpperCamelCase	:str		=      MarkupText(F"""The offloaded weights are all sent to the CPU."""					,    font_size=24      )
		step_a.move_to([2, 2, 0]      )

		self.play(Write(_UpperCAmelCase					,    run_time=3      )      )

		for i in range(6      ):
			UpperCamelCase	:Dict		=      cpu_disk_arr[i]
			UpperCamelCase	:List[str]		=      rect.copy().set_fill(_UpperCAmelCase					,    opacity=0.8      ).scale(2.0      )
			cpa.generate_target()
			cpa.target.move_to(model_base[i]      )

			if i == 0:
				rect.set_fill(_UpperCAmelCase					,    opacity=0.8      )
				rect.generate_target()
				rect.target.move_to(cpu_left_col_base[0]      ).scale(2.0      )

				self.remove(
				    *_UpperCAmelCase					,    *_UpperCAmelCase					,    )

			else:
				rect.generate_target()
				rect.target.move_to(cpu_left_col_base[i]      ).scale(2.0      )
			self.play(MoveToTarget(_UpperCAmelCase      )					,    MoveToTarget(_UpperCAmelCase      )					,    model_base[i].animate.set_stroke(_UpperCAmelCase      )      )
		self.play(FadeOut(_UpperCAmelCase      )      )

		UpperCamelCase	:List[str]		=      MarkupText(
		    F"""Finally, hooks are added to each weight in the model\nto transfer the weights from CPU to GPU\n\t\tand back when needed."""					,    font_size=24					,    )
		step_a.move_to([2, 2, 0]      )

		self.play(Write(_UpperCAmelCase					,    run_time=3      )      )

		UpperCamelCase	:Optional[int]		=      []
		UpperCamelCase	:Optional[Any]		=      []
		for i in range(6      ):
			UpperCamelCase	:List[Any]		=      Arrow(start=_UpperCAmelCase					,    end=_UpperCAmelCase					,    color=_UpperCAmelCase					,    buff=0.5      )
			a.next_to(model_base[i].get_left()					,    _UpperCAmelCase					,    buff=0.2      )
			arrows.append(_UpperCAmelCase      )
			animations.append(Write(_UpperCAmelCase      )      )
		self.play(*_UpperCAmelCase      )
		self.wait()








'''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  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase = True  ,		_UpperCAmelCase = "constant"  ,		_UpperCAmelCase = 0.0  ,		_UpperCAmelCase = "topK"  ,		):
             super(_UpperCAmelCase  ,		self						).__init__(in_features=_UpperCAmelCase  ,		out_features=_UpperCAmelCase  ,		bias=_UpperCAmelCase						)
             assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
             UpperCAmelCase__	:   Union[str, Any]												=	pruning_method

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




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