





"""simple docstring"""





import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer


def    a__       (       _SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		UpperCamelCase =						args.serialization_dir
		UpperCamelCase =						args.pruning_method
		UpperCamelCase =						args.threshold

		UpperCamelCase =						torch.load(os.path.join(_SCREAMING_SNAKE_CASE	,						"pytorch_model.bin"     )	,						map_location="cpu"     )

		UpperCamelCase =						0  # Number of remaining (not pruned) params in the encoder
		UpperCamelCase =						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":
								UpperCamelCase =						TopKBinarizer.apply(_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ).sum().item()
						elif pruning_method == "sigmoied_threshold":
								UpperCamelCase =						ThresholdBinarizer.apply(_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ).sum().item()
						elif pruning_method == "l0":
								UpperCamelCase ,					UpperCamelCase =						-0.1, 1.1
								UpperCamelCase =						torch.sigmoid(_SCREAMING_SNAKE_CASE     )
								UpperCamelCase =						s * (r - l) + l
								UpperCamelCase =						s_bar.clamp(min=0.0	,						max=1.0     )
								UpperCamelCase =						(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(_SCREAMING_SNAKE_CASE     )     )
				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)













import warnings

from ...utils import logging
from .image_processing_detr import DetrImageProcessor


_UpperCamelCase       :				Tuple           =			logging.get_logger(__name__)







class 		_lowerCAmelCase(		_a):

  """simple docstring"""







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

