

import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer




def        __UpperCamelCase     (      _A					:							str		)				->Any:



							"""simple docstring"""
							lowerCamelCase_								=args.serialization_dir
							lowerCamelCase_								=args.pruning_method
							lowerCamelCase_								=args.threshold

							lowerCamelCase_								=torch.load(os.path.join(lowercase__      ,  """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(lowercase__      ,  lowercase__		).sum().item()
																					elif pruning_method == "sigmoied_threshold":
																												lowerCamelCase_								=ThresholdBinarizer.apply(lowercase__      ,  lowercase__      ,  lowercase__		).sum().item()
																					elif pruning_method == "l0":
																												lowerCamelCase_	,   lowerCamelCase_								=-0.1, 1.1
																												lowerCamelCase_								=torch.sigmoid(lowercase__		)
																												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(100 * mask_ones / param.numel()      ,  3		)		).ljust(20      ,  """ """		)      ,  str(lowercase__		)		)
														else:
																					encoder_count += param.numel()
																					if "bias" in name or "LayerNorm" in name:
																												remaining_count += param.numel()

							print(""""""		)
							print("""Remaining Weights (global) %: """      ,  100 * remaining_count / encoder_count		)


if __name__ == "__main__":
	__A	:   Tuple	=					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',
	)

	__A	:   List[Any]	=					parser.parse_args()

	main(args)





'''simple docstring'''


import unittest
from dataclasses import dataclass
from typing import List, Union

import numpy as np
import PIL.Image

from diffusers.utils.outputs import BaseOutput







@dataclass
class 						UpperCAmelCase						(		__a):


 '''simple docstring'''




 __magic_name__       :			Union[List[PIL.Image.Image], np.ndarray]







class 						UpperCAmelCase						(		unittest.TestCase):


 '''simple docstring'''




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



      """simple docstring"""







      a_									=CustomOutput(images=np.random.rand(1				,	3				,	4				,	4))

      # check every way of getting the attribute
      assert isinstance(outputs.images				,	np.ndarray)
      assert outputs.images.shape == (1, 3, 4, 4)
      assert isinstance(outputs["images"]				,	np.ndarray)
      assert outputs["images"].shape == (1, 3, 4, 4)
      assert isinstance(outputs[0]				,	np.ndarray)
      assert outputs[0].shape == (1, 3, 4, 4)

      # test with a non-tensor attribute
      a_									=CustomOutput(images=[PIL.Image.new("RGB"				,	(4, 4))])

      # check every way of getting the attribute
      assert isinstance(outputs.images				,	lowerCAmelCase_)
      assert isinstance(outputs.images[0]				,	PIL.Image.Image)
      assert isinstance(outputs["images"]				,	lowerCAmelCase_)
      assert isinstance(outputs["images"][0]				,	PIL.Image.Image)
      assert isinstance(outputs[0]				,	lowerCAmelCase_)
      assert isinstance(outputs[0][0]				,	PIL.Image.Image)




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



      """simple docstring"""







      a_									=CustomOutput({"images": np.random.rand(1				,	3				,	4				,	4)})

      # check every way of getting the attribute
      assert isinstance(outputs.images				,	np.ndarray)
      assert outputs.images.shape == (1, 3, 4, 4)
      assert isinstance(outputs["images"]				,	np.ndarray)
      assert outputs["images"].shape == (1, 3, 4, 4)
      assert isinstance(outputs[0]				,	np.ndarray)
      assert outputs[0].shape == (1, 3, 4, 4)

      # test with a non-tensor attribute
      a_									=CustomOutput({"images": [PIL.Image.new("RGB"				,	(4, 4))]})

      # check every way of getting the attribute
      assert isinstance(outputs.images				,	lowerCAmelCase_)
      assert isinstance(outputs.images[0]				,	PIL.Image.Image)
      assert isinstance(outputs["images"]				,	lowerCAmelCase_)
      assert isinstance(outputs["images"][0]				,	PIL.Image.Image)
      assert isinstance(outputs[0]				,	lowerCAmelCase_)
      assert isinstance(outputs[0][0]				,	PIL.Image.Image)


