





'''simple docstring'''



from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, is_invisible_watermark_available, is_torch_available, is_transformers_available

@dataclass
class     A_ (  a__  ):

			'''simple docstring'''





			_lowerCAmelCase						=    42


if is_transformers_available() and is_torch_available() and is_invisible_watermark_available():
			from .pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
			from .pipeline_stable_diffusion_xl_imgaimg import StableDiffusionXLImgaImgPipeline







'''simple docstring'''



import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer

class     A_ (  nn.Linear  ):

			'''simple docstring'''





			def __init__(					self			,     A_			,     A_			,     A_ = True			,     A_ = "constant"			,     A_ = 0.0			,     A_ = "topK"			,     ):
								super(A_			,     self   ).__init__(in_features=A_			,     out_features=A_			,     bias=A_   )
								assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
								_UpperCamelCase											=   pruning_method

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




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



