



"""simple docstring"""


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection

from diffusers import (
    AutoencoderKL,
    DPMSolverMultistepScheduler,
    PNDMScheduler,
    StableDiffusionImageVariationPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class  A_     (	SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_      ,  unittest.TestCase							):



						"""simple docstring"""
						__UpperCamelCase													=				StableDiffusionImageVariationPipeline
						__UpperCamelCase													=				IMAGE_VARIATION_PARAMS
						__UpperCamelCase													=				IMAGE_VARIATION_BATCH_PARAMS
						__UpperCamelCase													=				frozenset([]							)
						# TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
						__UpperCamelCase													=				frozenset([]							)






						def       UpperCAmelCase__	(   self					:Optional[int]   )							->     str:
												torch.manual_seed(0   )
												UpperCAmelCase			 =					UNetaDConditionModel(
												    block_out_channels=(32, 64)  ,    layers_per_block=2  ,    sample_size=32  ,    in_channels=4  ,    out_channels=4  ,    down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')  ,    up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')  ,    cross_attention_dim=32  ,    )
												UpperCAmelCase			 =					PNDMScheduler(skip_prk_steps=lowercase_   )
												torch.manual_seed(0   )
												UpperCAmelCase			 =					AutoencoderKL(
												    block_out_channels=[32, 64]  ,    in_channels=3  ,    out_channels=3  ,    down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']  ,    up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']  ,    latent_channels=4  ,    )
												torch.manual_seed(0   )
												UpperCAmelCase			 =					CLIPVisionConfig(
												    hidden_size=32  ,    projection_dim=32  ,    intermediate_size=37  ,    layer_norm_eps=1E-05  ,    num_attention_heads=4  ,    num_hidden_layers=5  ,    image_size=32  ,    patch_size=4  ,    )
												UpperCAmelCase			 =					CLIPVisionModelWithProjection(lowercase_   )
												UpperCAmelCase			 =					CLIPImageProcessor(crop_size=32  ,    size=32   )

												UpperCAmelCase			 =					{
												    'unet': unet,
												    'scheduler': scheduler,
												    'vae': vae,
												    'image_encoder': image_encoder,
												    'feature_extractor': feature_extractor,
												    'safety_checker': None,
												}
												return components






						def       UpperCAmelCase__	(   self					:Any  ,    lowercase_					:Optional[Any]  ,    lowercase_					:int=0   )							->     Optional[int]:
												UpperCAmelCase			 =					floats_tensor((1, 3, 32, 32)  ,    rng=random.Random(lowercase_   )   )
												UpperCAmelCase			 =					image.cpu().permute(0  ,    2  ,    3  ,    1   )[0]
												UpperCAmelCase			 =					Image.fromarray(np.uinta(lowercase_   )   ).convert('RGB'   ).resize((32, 32)   )
												if str(lowercase_   ).startswith('mps'   ):
																		UpperCAmelCase			 =					torch.manual_seed(lowercase_   )
												else:
																		UpperCAmelCase			 =					torch.Generator(device=lowercase_   ).manual_seed(lowercase_   )
												UpperCAmelCase			 =					{
												    'image': image,
												    'generator': generator,
												    'num_inference_steps': 2,
												    'guidance_scale': 6.0,
												    'output_type': 'numpy',
												}
												return inputs






						def       UpperCAmelCase__	(   self					:Tuple   )							->     str:
												UpperCAmelCase			 =					'cpu'  # ensure determinism for the device-dependent torch.Generator
												UpperCAmelCase			 =					self.get_dummy_components()
												UpperCAmelCase			 =					StableDiffusionImageVariationPipeline(**lowercase_   )
												UpperCAmelCase			 =					sd_pipe.to(lowercase_   )
												sd_pipe.set_progress_bar_config(disable=lowercase_   )

												UpperCAmelCase			 =					self.get_dummy_inputs(lowercase_   )
												UpperCAmelCase			 =					sd_pipe(**lowercase_   ).images
												UpperCAmelCase			 =					image[0, -3:, -3:, -1]

												assert image.shape == (1, 64, 64, 3)
												UpperCAmelCase			 =					np.array([0.5239, 0.5723, 0.4796, 0.5049, 0.5550, 0.4685, 0.5329, 0.4891, 0.4921]   )

												assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-3






						def       UpperCAmelCase__	(   self					:Dict   )							->     Dict:
												UpperCAmelCase			 =					'cpu'  # ensure determinism for the device-dependent torch.Generator
												UpperCAmelCase			 =					self.get_dummy_components()
												UpperCAmelCase			 =					StableDiffusionImageVariationPipeline(**lowercase_   )
												UpperCAmelCase			 =					sd_pipe.to(lowercase_   )
												sd_pipe.set_progress_bar_config(disable=lowercase_   )

												UpperCAmelCase			 =					self.get_dummy_inputs(lowercase_   )
												UpperCAmelCase			 =					2 * [inputs['image']]
												UpperCAmelCase			 =					sd_pipe(**lowercase_   )

												UpperCAmelCase			 =					output.images

												UpperCAmelCase			 =					image[-1, -3:, -3:, -1]

												assert image.shape == (2, 64, 64, 3)
												UpperCAmelCase			 =					np.array([0.6892, 0.5637, 0.5836, 0.5771, 0.6254, 0.6409, 0.5580, 0.5569, 0.5289]   )

												assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-3






						def       UpperCAmelCase__	(   self					:Union[str, Any]   )							->     List[str]:
												super().test_inference_batch_single_identical(expected_max_diff=3E-3   )







@slow
@require_torch_gpu
class  A_     (	unittest.TestCase							):



						"""simple docstring"""






						def       UpperCAmelCase__	(   self					:Optional[Any]   )							->     List[Any]:
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()






						def       UpperCAmelCase__	(   self					:List[Any]  ,    lowercase_					:List[Any]  ,    lowercase_					:int="cpu"  ,    lowercase_					:Optional[Any]=torch.floataa  ,    lowercase_					:List[str]=0   )							->     Any:
												UpperCAmelCase			 =					torch.Generator(device=lowercase_   ).manual_seed(lowercase_   )
												UpperCAmelCase			 =					load_image(
												    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
												    '/stable_diffusion_imgvar/input_image_vermeer.png'   )
												UpperCAmelCase			 =					np.random.RandomState(lowercase_   ).standard_normal((1, 4, 64, 64)   )
												UpperCAmelCase			 =					torch.from_numpy(lowercase_   ).to(device=lowercase_  ,    dtype=lowercase_   )
												UpperCAmelCase			 =					{
												    'image': init_image,
												    'latents': latents,
												    'generator': generator,
												    'num_inference_steps': 3,
												    'guidance_scale': 7.5,
												    'output_type': 'numpy',
												}
												return inputs






						def       UpperCAmelCase__	(   self					:Optional[Any]   )							->     List[str]:
												UpperCAmelCase			 =					StableDiffusionImageVariationPipeline.from_pretrained(
												    'lambdalabs/sd-image-variations-diffusers'  ,    safety_checker=lowercase_   )
												UpperCAmelCase			 =					sd_pipe.to(lowercase_   )
												sd_pipe.set_progress_bar_config(disable=lowercase_   )

												UpperCAmelCase			 =					self.get_inputs(lowercase_   )
												UpperCAmelCase			 =					sd_pipe(**lowercase_   ).images
												UpperCAmelCase			 =					image[0, -3:, -3:, -1].flatten()

												assert image.shape == (1, 5_12, 5_12, 3)
												UpperCAmelCase			 =					np.array([0.8_4491, 0.9_0789, 0.7_5708, 0.7_8734, 0.8_3485, 0.7_0099, 0.6_6938, 0.6_8727, 0.6_1379]   )
												assert np.abs(image_slice - expected_slice   ).max() < 6E-3






						def       UpperCAmelCase__	(   self					:Optional[int]   )							->     Dict:
												UpperCAmelCase			 =					0

												def callback_fn(lowercase_					:int  ,    lowercase_					:int  ,    lowercase_					:torch.FloatTensor   ) -> None:
																		UpperCAmelCase			 =					True
																		nonlocal number_of_steps
																		number_of_steps += 1
																		if step == 1:
																								UpperCAmelCase			 =					latents.detach().cpu().numpy()
																								assert latents.shape == (1, 4, 64, 64)
																								UpperCAmelCase			 =					latents[0, -3:, -3:, -1]
																								UpperCAmelCase			 =					np.array(
																								    [-0.1621, 0.2837, -0.7979, -0.1221, -1.3057, 0.7681, -2.1191, 0.0464, 1.6309]   )

																								assert np.abs(latents_slice.flatten() - expected_slice   ).max() < 5E-2
																		elif step == 2:
																								UpperCAmelCase			 =					latents.detach().cpu().numpy()
																								assert latents.shape == (1, 4, 64, 64)
																								UpperCAmelCase			 =					latents[0, -3:, -3:, -1]
																								UpperCAmelCase			 =					np.array([0.6299, 1.7500, 1.1992, -2.1582, -1.8994, 0.7334, -0.7090, 1.0137, 1.5273]   )

																								assert np.abs(latents_slice.flatten() - expected_slice   ).max() < 5E-2

												UpperCAmelCase			 =					False

												UpperCAmelCase			 =					StableDiffusionImageVariationPipeline.from_pretrained(
												    'fusing/sd-image-variations-diffusers'  ,    safety_checker=lowercase_  ,    torch_dtype=torch.floataa  ,    )
												pipe.to(lowercase_   )
												pipe.set_progress_bar_config(disable=lowercase_   )
												pipe.enable_attention_slicing()

												UpperCAmelCase			 =					self.get_inputs(lowercase_  ,    dtype=torch.floataa   )
												pipe(**lowercase_  ,    callback=lowercase_  ,    callback_steps=1   )
												assert callback_fn.has_been_called
												assert number_of_steps == inputs["num_inference_steps"]






						def       UpperCAmelCase__	(   self					:List[Any]   )							->     str:
												torch.cuda.empty_cache()
												torch.cuda.reset_max_memory_allocated()
												torch.cuda.reset_peak_memory_stats()

												UpperCAmelCase			 =					'fusing/sd-image-variations-diffusers'
												UpperCAmelCase			 =					StableDiffusionImageVariationPipeline.from_pretrained(
												    lowercase_  ,    safety_checker=lowercase_  ,    torch_dtype=torch.floataa   )
												UpperCAmelCase			 =					pipe.to(lowercase_   )
												pipe.set_progress_bar_config(disable=lowercase_   )
												pipe.enable_attention_slicing(1   )
												pipe.enable_sequential_cpu_offload()

												UpperCAmelCase			 =					self.get_inputs(lowercase_  ,    dtype=torch.floataa   )
												UpperCAmelCase			 =					pipe(**lowercase_   )

												UpperCAmelCase			 =					torch.cuda.max_memory_allocated()
												# make sure that less than 2.6 GB is allocated
												assert mem_bytes < 2.6 * 10**9







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



						"""simple docstring"""






						def       UpperCAmelCase__	(   self					:List[str]   )							->     Optional[Any]:
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()






						def       UpperCAmelCase__	(   self					:Dict  ,    lowercase_					:List[str]  ,    lowercase_					:int="cpu"  ,    lowercase_					:Tuple=torch.floataa  ,    lowercase_					:str=0   )							->     Optional[Any]:
												UpperCAmelCase			 =					torch.Generator(device=lowercase_   ).manual_seed(lowercase_   )
												UpperCAmelCase			 =					load_image(
												    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
												    '/stable_diffusion_imgvar/input_image_vermeer.png'   )
												UpperCAmelCase			 =					np.random.RandomState(lowercase_   ).standard_normal((1, 4, 64, 64)   )
												UpperCAmelCase			 =					torch.from_numpy(lowercase_   ).to(device=lowercase_  ,    dtype=lowercase_   )
												UpperCAmelCase			 =					{
												    'image': init_image,
												    'latents': latents,
												    'generator': generator,
												    'num_inference_steps': 50,
												    'guidance_scale': 7.5,
												    'output_type': 'numpy',
												}
												return inputs






						def       UpperCAmelCase__	(   self					:Optional[Any]   )							->     Tuple:
												UpperCAmelCase			 =					StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'   )
												sd_pipe.to(lowercase_   )
												sd_pipe.set_progress_bar_config(disable=lowercase_   )

												UpperCAmelCase			 =					self.get_inputs(lowercase_   )
												UpperCAmelCase			 =					sd_pipe(**lowercase_   ).images[0]

												UpperCAmelCase			 =					load_numpy(
												    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
												    '/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy'   )
												UpperCAmelCase			 =					np.abs(expected_image - image   ).max()
												assert max_diff < 1E-3






						def       UpperCAmelCase__	(   self					:str   )							->     List[str]:
												UpperCAmelCase			 =					StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'   )
												UpperCAmelCase			 =					DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config   )
												sd_pipe.to(lowercase_   )
												sd_pipe.set_progress_bar_config(disable=lowercase_   )

												UpperCAmelCase			 =					self.get_inputs(lowercase_   )
												UpperCAmelCase			 =					25
												UpperCAmelCase			 =					sd_pipe(**lowercase_   ).images[0]

												UpperCAmelCase			 =					load_numpy(
												    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
												    '/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy'   )
												UpperCAmelCase			 =					np.abs(expected_image - image   ).max()
												assert max_diff < 1E-3










'''simple docstring'''


import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer






def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







     a_									=args.serialization_dir
     a_									=args.pruning_method
     a_									=args.threshold

     a_									=torch.load(os.path.join(lowercase__							,						"pytorch_model.bin"							)							,						map_location="cpu"							)

     a_									=0  # Number of remaining (not pruned) params in the encoder
     a_									=0  # Number of params in the encoder

     print("name".ljust(6_0							,						" "							)							,						"Remaining Weights %"							,						"Remaining Weight"							)
     for name, param in st.items():
          if "encoder" not in name:
               continue

          if "mask_scores" in name:
               if pruning_method == "topK":
                    a_									=TopKBinarizer.apply(lowercase__							,						lowercase__							).sum().item()
               elif pruning_method == "sigmoied_threshold":
                    a_									=ThresholdBinarizer.apply(lowercase__							,						lowercase__							,						lowercase__							).sum().item()
               elif pruning_method == "l0":
                    a_		,    a_									=-0.1, 1.1
                    a_									=torch.sigmoid(lowercase__							)
                    a_									=s * (r - l) + l
                    a_									=s_bar.clamp(min=0.0							,						max=1.0							)
                    a_									=(mask > 0.0).sum().item()
               else:
                    raise ValueError("Unknown pruning method"							)
               remaining_count += mask_ones
               print(name.ljust(6_0							,						" "							)							,						str(round(1_0_0 * mask_ones / param.numel()							,						3							)							).ljust(2_0							,						" "							)							,						str(lowercase__							)							)
          else:
               encoder_count += param.numel()
               if "bias" in name or "LayerNorm" in name:
                    remaining_count += param.numel()

     print(""							)
     print("Remaining Weights (global) %: "							,						1_0_0 * remaining_count / encoder_count							)


if __name__ == "__main__":
  lowercase											=      argparse.ArgumentParser()

  parser.add_argument(
      '''--pruning_method''',
      choices=['''l0''', '''topK''', '''sigmoied_threshold'''],
      type=str,
      required=True,
      help=(
          '''Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement'''
          ''' pruning)'''
      ),
  )
  parser.add_argument(
      '''--threshold''',
      type=float,
      required=False,
      help=(
          '''For `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'''
          '''For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'''
          '''Not needed for `l0`'''
      ),
  )
  parser.add_argument(
      '''--serialization_dir''',
      type=str,
      required=True,
      help='''Folder containing the model that was previously fine-pruned''',
  )

  lowercase											=      parser.parse_args()

  main(args)


