import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer




class 				__lowerCAmelCase					(    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"    ,     )	->							List[Any]:
        super(lowerCamelCase    ,     self					).__init__(in_features=lowerCamelCase    ,     out_features=lowerCamelCase    ,     bias=lowerCamelCase					)
        assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
        _UpperCamelCase       :    List[Any]					  =					pruning_method

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

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


UpperCAmelCase     						=			False




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



 '''simple docstring'''

 def    __snake_case   (     self    :						Optional[Any]					)	->							str:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

 def    __snake_case   (     self    :						Dict					)	->							str:
        _UpperCamelCase       :    Dict					  =					VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'					)
        # remove text_unet
        pipe.remove_unused_weights()
        pipe.to(lowerCamelCase					)
        pipe.set_progress_bar_config(disable=lowerCamelCase					)

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

        _UpperCamelCase       :    Optional[int]					  =					torch.manual_seed(0					)
        _UpperCamelCase       :    Optional[Any]					  =					pipe(
            prompt='first prompt'    ,     image=lowerCamelCase    ,     text_to_image_strength=0.7_5    ,     generator=lowerCamelCase    ,     guidance_scale=7.5    ,     num_inference_steps=2    ,     output_type='numpy'    ,     ).images

        with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(lowerCamelCase					)
               _UpperCamelCase       :    int					  =					VersatileDiffusionDualGuidedPipeline.from_pretrained(lowerCamelCase					)

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

        _UpperCamelCase       :    str					  =					generator.manual_seed(0					)
        _UpperCamelCase       :    Tuple					  =					pipe(
            prompt='first prompt'    ,     image=lowerCamelCase    ,     text_to_image_strength=0.7_5    ,     generator=lowerCamelCase    ,     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    __snake_case   (     self    :						Tuple					)	->							Optional[int]:
        _UpperCamelCase       :    Optional[Any]					  =					VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'					)
        pipe.remove_unused_weights()
        pipe.to(lowerCamelCase					)
        pipe.set_progress_bar_config(disable=lowerCamelCase					)

        _UpperCamelCase       :    Union[str, Any]					  =					'cyberpunk 2077'
        _UpperCamelCase       :    str					  =					load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'					)
        _UpperCamelCase       :    List[Any]					  =					torch.manual_seed(0					)
        _UpperCamelCase       :    Optional[Any]					  =					pipe(
            prompt=lowerCamelCase    ,     image=lowerCamelCase    ,     text_to_image_strength=0.7_5    ,     generator=lowerCamelCase    ,     guidance_scale=7.5    ,     num_inference_steps=5_0    ,     output_type='numpy'    ,     ).images

        _UpperCamelCase       :    Any					  =					image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        _UpperCamelCase       :    Optional[int]					  =					np.array([0.0_7_8_7, 0.0_8_4_9, 0.0_8_2_6, 0.0_8_1_2, 0.0_8_0_7, 0.0_7_9_5, 0.0_8_1_8, 0.0_7_9_8, 0.0_7_7_9]					)

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



