





import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer

class 					__A   (  nn.Linear			):


				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"]
								lowerCamelCase		       =pruning_method

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


				def _snake_case				(						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 _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_							):
								# Get the mask
								if self.pruning_method == "topK":
												lowerCamelCase		       =TopKBinarizer.apply(self.mask_scores      ,       UpperCAmelCase_							)
								elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
												lowerCamelCase		       ="""sigmoied""" in self.pruning_method
												lowerCamelCase		       =ThresholdBinarizer.apply(self.mask_scores      ,       UpperCAmelCase_      ,       UpperCAmelCase_							)
								elif self.pruning_method == "magnitude":
												lowerCamelCase		       =MagnitudeBinarizer.apply(self.weight      ,       UpperCAmelCase_							)
								elif self.pruning_method == "l0":
												lowerCamelCase       ,     lowerCamelCase       ,     lowerCamelCase		       =-0.1, 1.1, 2 / 3
												if self.training:
																lowerCamelCase		       =torch.zeros_like(self.mask_scores							).uniform_().clamp(0.0_0_0_1      ,       0.9_9_9_9							)
																lowerCamelCase		       =torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b							)
												else:
																lowerCamelCase		       =torch.sigmoid(self.mask_scores							)
												lowerCamelCase		       =s * (r - l) + l
												lowerCamelCase		       =s_bar.clamp(min=0.0      ,       max=1.0							)
								# Mask weights with computed mask
								lowerCamelCase		       =mask * self.weight
								# Compute output (linear layer) with masked weights
								return nn.functional.linear(UpperCAmelCase_      ,       UpperCAmelCase_      ,       self.bias							)












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			):
				__A            = 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





