






"""simple docstring"""
import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer





def 							lowercase__		(				lowerCamelCase					):
				_SCREAMING_SNAKE_CASE      :		str							=						args.serialization_dir
				_SCREAMING_SNAKE_CASE      :		Optional[int]							=						args.pruning_method
				_SCREAMING_SNAKE_CASE      :		int							=						args.threshold

				_SCREAMING_SNAKE_CASE      :		str							=						torch.load(os.path.join(lowerCamelCase,	'pytorch_model.bin'					),	map_location='cpu'					)

				_SCREAMING_SNAKE_CASE      :		List[Any]							=						0  # Number of remaining (not pruned) params in the encoder
				_SCREAMING_SNAKE_CASE      :		List[str]							=						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":
																_SCREAMING_SNAKE_CASE      :		Tuple							=						TopKBinarizer.apply(lowerCamelCase,	lowerCamelCase					).sum().item()
												elif pruning_method == "sigmoied_threshold":
																_SCREAMING_SNAKE_CASE      :		Tuple							=						ThresholdBinarizer.apply(lowerCamelCase,	lowerCamelCase,	lowerCamelCase					).sum().item()
												elif pruning_method == "l0":
																_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      :		Optional[int]							=						-0.1, 1.1
																_SCREAMING_SNAKE_CASE      :		Dict							=						torch.sigmoid(lowerCamelCase					)
																_SCREAMING_SNAKE_CASE      :		List[str]							=						s * (r - l) + l
																_SCREAMING_SNAKE_CASE      :		Tuple							=						s_bar.clamp(min=0.0,	max=1.0					)
																_SCREAMING_SNAKE_CASE      :		List[str]							=						(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(lowerCamelCase					)					)
								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__":
			lowerCAmelCase__							= 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',
			)

			lowerCAmelCase__							= parser.parse_args()

			main(args)









"""simple docstring"""
import warnings

from ...utils import logging
from .image_processing_vit import ViTImageProcessor


lowerCAmelCase__							= logging.get_logger(__name__)


class _lowerCAmelCase	(						__UpperCAmelCase					):







						def __init__(						self      ,							*lowerCAmelCase_      ,							**lowerCAmelCase_       )		->       None:
										warnings.warn(
										    'The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
										    ' use ViTImageProcessor instead.'      ,							lowerCAmelCase_      ,							)
										super().__init__(*lowerCAmelCase_      ,							**lowerCAmelCase_       )

