





import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer



def _a							(      lowerCamelCase:  Optional[Any]					)      ->		Optional[int]:







      '''simple docstring'''






      __A			  =							args.serialization_dir
      __A			  =							args.pruning_method
      __A			  =							args.threshold

      __A			  =							torch.load(os.path.join(lowerCamelCase   ,							'''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(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":
                        __A			  =							TopKBinarizer.apply(lowerCamelCase   ,							lowerCamelCase					).sum().item()
                  elif pruning_method == "sigmoied_threshold":
                        __A			  =							ThresholdBinarizer.apply(lowerCamelCase   ,							lowerCamelCase   ,							lowerCamelCase					).sum().item()
                  elif pruning_method == "l0":
                        __A  ,		__A			  =							-0.1, 1.1
                        __A			  =							torch.sigmoid(lowerCamelCase					)
                        __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(60   ,							''' '''					)   ,							str(round(1_00 * 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) %: '''   ,							1_00 * remaining_count / encoder_count					)


if __name__ == "__main__":
       snake_case__   :				Any						=	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',
       )

       snake_case__   :				Optional[Any]						=	parser.parse_args()

       main(args)












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


snake_case__   :				Dict						=	{
    'configuration_videomae': ['VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VideoMAEConfig'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				Any						=	[
           'VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST',
           'VideoMAEForPreTraining',
           'VideoMAEModel',
           'VideoMAEPreTrainedModel',
           'VideoMAEForVideoClassification',
       ]

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				str						=	['VideoMAEFeatureExtractor']
       snake_case__   :				Tuple						=	['VideoMAEImageProcessor']

if TYPE_CHECKING:
       from .configuration_videomae import VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP, VideoMAEConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_videomae import (
                  VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  VideoMAEForPreTraining,
                  VideoMAEForVideoClassification,
                  VideoMAEModel,
                  VideoMAEPreTrainedModel,
              )

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_videomae import VideoMAEFeatureExtractor
              from .image_processing_videomae import VideoMAEImageProcessor

else:
       import sys

       snake_case__   :				Optional[Any]						=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





