

import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer






class 			__magic_name__					(    nn.Linear   ):





			def __init__(	self      ,						__snake_case      ,						__snake_case      ,						__snake_case = True      ,						__snake_case = "constant"      ,						__snake_case = 0.0      ,						__snake_case = "topK"      ,						)     ->				Dict:




						'''simple docstring'''
						super(__snake_case      ,						self      ).__init__(in_features=__snake_case      ,						out_features=__snake_case      ,						bias=__snake_case      )
						assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
						__a						   =pruning_method

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





			def 	__magic_name__					(	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 	__magic_name__					(	self      ,						__snake_case      ,						__snake_case      )     ->				Optional[int]:




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









import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_lowerCAmelCase      :    List[str]						       =    False






@nightly
@require_torch_gpu
class 			__magic_name__					(    unittest.TestCase   ):





			def 	__magic_name__					(	self      )     ->				Optional[Any]:




						'''simple docstring'''
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()





			def 	__magic_name__					(	self      )     ->				Tuple:




						'''simple docstring'''
						__a						   =VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'      )
						# remove text_unet
						pipe.remove_unused_weights()
						pipe.to(__snake_case      )
						pipe.set_progress_bar_config(disable=__snake_case      )

						__a						   =load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'      )

						__a						   =torch.manual_seed(0      )
						__a						   =pipe(
						    prompt='first prompt'      ,						image=__snake_case      ,						text_to_image_strength=0.75      ,						generator=__snake_case      ,						guidance_scale=7.5      ,						num_inference_steps=2      ,						output_type='numpy'      ,						).images

						with tempfile.TemporaryDirectory() as tmpdirname:
									pipe.save_pretrained(__snake_case      )
									__a						   =VersatileDiffusionDualGuidedPipeline.from_pretrained(__snake_case      )

						pipe.to(__snake_case      )
						pipe.set_progress_bar_config(disable=__snake_case      )

						__a						   =generator.manual_seed(0      )
						__a						   =pipe(
						    prompt='first prompt'      ,						image=__snake_case      ,						text_to_image_strength=0.75      ,						generator=__snake_case      ,						guidance_scale=7.5      ,						num_inference_steps=2      ,						output_type='numpy'      ,						).images

						assert np.abs(image - new_image      ).sum() < 1e-5, "Models don't have the same forward pass"





			def 	__magic_name__					(	self      )     ->				Optional[Any]:




						'''simple docstring'''
						__a						   =VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'      )
						pipe.remove_unused_weights()
						pipe.to(__snake_case      )
						pipe.set_progress_bar_config(disable=__snake_case      )

						__a						   ='cyberpunk 2077'
						__a						   =load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'      )
						__a						   =torch.manual_seed(0      )
						__a						   =pipe(
						    prompt=__snake_case      ,						image=__snake_case      ,						text_to_image_strength=0.75      ,						generator=__snake_case      ,						guidance_scale=7.5      ,						num_inference_steps=50      ,						output_type='numpy'      ,						).images

						__a						   =image[0, 253:256, 253:256, -1]

						assert image.shape == (1, 512, 512, 3)
						__a						   =np.array([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779]      )

						assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2






