





import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer





def   _lowercase       (    _UpperCAmelCase					)    ->	Union[str, Any]:
				lowerCamelCase									=args.serialization_dir
				lowerCamelCase									=args.pruning_method
				lowerCamelCase									=args.threshold

				lowerCamelCase									=torch.load(os.path.join(_UpperCAmelCase      ,   """pytorch_model.bin"""					)      ,   map_location="""cpu"""					)

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

				print("""name""".ljust(60      ,   """ """					)      ,   """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":
																lowerCamelCase									=TopKBinarizer.apply(_UpperCAmelCase      ,   _UpperCAmelCase					).sum().item()
												elif pruning_method == "sigmoied_threshold":
																lowerCamelCase									=ThresholdBinarizer.apply(_UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase					).sum().item()
												elif pruning_method == "l0":
																lowerCamelCase			,	lowerCamelCase									=-0.1, 1.1
																lowerCamelCase									=torch.sigmoid(_UpperCAmelCase					)
																lowerCamelCase									=s * (r - l) + l
																lowerCamelCase									=s_bar.clamp(min=0.0      ,   max=1.0					)
																lowerCamelCase									=(mask > 0.0).sum().item()
												else:
																raise ValueError("""Unknown pruning method"""					)
												remaining_count += mask_ones
												print(name.ljust(60      ,   """ """					)      ,   str(round(1_00 * mask_ones / param.numel()      ,   3					)					).ljust(20      ,   """ """					)      ,   str(_UpperCAmelCase					)					)
								else:
												encoder_count += param.numel()
												if "bias" in name or "LayerNorm" in name:
																remaining_count += param.numel()

				print(""""""					)
				print("""Remaining Weights (global) %: """      ,   1_00 * remaining_count / encoder_count					)


if __name__ == "__main__":
							UpperCAmelCase__    :      Dict       					=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''',
							)

							UpperCAmelCase__    :      Tuple       					=parser.parse_args()

							main(args)







'''simple docstring'''





import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class 			UpperCamelCase__	(     SCREAMING_SNAKE_CASE   ,  unittest.TestCase):
 UpperCAmelCase__     :     Union[str, Any]    					=       RePaintPipeline
 UpperCAmelCase__     :     Any    					=       IMAGE_INPAINTING_PARAMS - {'width', 'height', 'guidance_scale'}
 UpperCAmelCase__     :     Optional[int]    					=       PipelineTesterMixin.required_optional_params - {
     'latents',
     'num_images_per_prompt',
     'callback',
     'callback_steps',
 }
 UpperCAmelCase__     :     Optional[int]    					=       IMAGE_INPAINTING_BATCH_PARAMS
 UpperCAmelCase__     :     Any    					=       False






 def 						lowercase_    (      self	:Any      )					->      int:







       '''simple docstring'''



       torch.manual_seed(0      )
       torch.manual_seed(0      )
       __A													=      UNetaDModel(
           block_out_channels=(32, 64)						,					layers_per_block=2						,					sample_size=32						,					in_channels=3						,					out_channels=3						,					down_block_types=('DownBlock2D', 'AttnDownBlock2D')						,					up_block_types=('AttnUpBlock2D', 'UpBlock2D')						,					)
       __A													=      RePaintScheduler()
       __A													=      {'unet': unet, 'scheduler': scheduler}
       return components






 def 						lowercase_    (      self	:Any						,					_A	:List[Any]						,					_A	:Tuple=0      )					->      List[str]:







       '''simple docstring'''



       if str(_A      ).startswith('mps'      ):
             __A													=      torch.manual_seed(_A      )
       else:
             __A													=      torch.Generator(device=_A      ).manual_seed(_A      )
       __A													=      np.random.RandomState(_A      ).standard_normal((1, 3, 32, 32)      )
       __A													=      torch.from_numpy(_A      ).to(device=_A						,					dtype=torch.floataa      )
       __A													=      (image > 0).to(device=_A						,					dtype=torch.floataa      )
       __A													=      {
           'image': image,
           'mask_image': mask,
           'generator': generator,
           'num_inference_steps': 5,
           'eta': 0.0,
           'jump_length': 2,
           'jump_n_sample': 2,
           'output_type': 'numpy',
       }
       return inputs






 def 						lowercase_    (      self	:Union[str, Any]      )					->      Optional[int]:







       '''simple docstring'''



       __A													=      'cpu'  # ensure determinism for the device-dependent torch.Generator
       __A													=      self.get_dummy_components()
       __A													=      RePaintPipeline(**_A      )
       __A													=      sd_pipe.to(_A      )
       sd_pipe.set_progress_bar_config(disable=_A      )

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

       assert image.shape == (1, 32, 32, 3)
       __A													=      np.array([1.0_000, 0.5_426, 0.5_497, 0.2_200, 1.0_000, 1.0_000, 0.5_623, 1.0_000, 0.6_274]      )

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






 @skip_mps
 def 						lowercase_    (      self	:Dict      )					->      Optional[int]:







       '''simple docstring'''



       return super().test_save_load_local()






 @unittest.skip('non-deterministic pipeline'      )
 def 						lowercase_    (      self	:Union[str, Any]      )					->      int:







       '''simple docstring'''



       return super().test_inference_batch_single_identical()






 @skip_mps
 def 						lowercase_    (      self	:Dict      )					->      Union[str, Any]:







       '''simple docstring'''



       return super().test_dict_tuple_outputs_equivalent()






 @skip_mps
 def 						lowercase_    (      self	:List[str]      )					->      int:







       '''simple docstring'''



       return super().test_save_load_optional_components()






 @skip_mps
 def 						lowercase_    (      self	:Optional[int]      )					->      Optional[Any]:







       '''simple docstring'''



       return super().test_attention_slicing_forward_pass()


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






 def 						lowercase_    (      self	:Optional[int]      )					->      int:







       '''simple docstring'''



       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






 def 						lowercase_    (      self	:Dict      )					->      Optional[Any]:







       '''simple docstring'''



       __A													=      load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
           'repaint/celeba_hq_256.png'      )
       __A													=      load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'      )
       __A													=      load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
           'repaint/celeba_hq_256_result.npy'      )

       __A													=      'google/ddpm-ema-celebahq-256'
       __A													=      UNetaDModel.from_pretrained(_A      )
       __A													=      RePaintScheduler.from_pretrained(_A      )

       __A													=      RePaintPipeline(unet=_A						,					scheduler=_A      ).to(_A      )
       repaint.set_progress_bar_config(disable=_A      )
       repaint.enable_attention_slicing()

       __A													=      torch.manual_seed(0      )
       __A													=      repaint(
           _A						,					_A						,					num_inference_steps=250						,					eta=0.0						,					jump_length=10						,					jump_n_sample=10						,					generator=_A						,					output_type='np'						,					)
       __A													=      output.images[0]

       assert image.shape == (256, 256, 3)
       assert np.abs(expected_image - image      ).mean() < 1E-2






