






import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer






def        lowerCamelCase__   (					__lowerCamelCase    :  Any	):
  __UpperCAmelCase							:			Any     =						args.serialization_dir
  __UpperCAmelCase							:			str     =						args.pruning_method
  __UpperCAmelCase							:			Any     =						args.threshold

  __UpperCAmelCase							:			str     =						torch.load(os.path.join(__lowerCamelCase						,	"""pytorch_model.bin"""	)						,	map_location="""cpu"""	)

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

       a	: str      =				parser.parse_args()

       main(args)










from typing import TYPE_CHECKING

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


a	: str      =				{"configuration_hubert": ["HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "HubertConfig"]}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: str      =				[
           "HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "HubertForCTC",
           "HubertForSequenceClassification",
           "HubertModel",
           "HubertPreTrainedModel",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: List[str]      =				[
           "TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFHubertForCTC",
           "TFHubertModel",
           "TFHubertPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_hubert import (
                  HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  HubertForCTC,
                  HubertForSequenceClassification,
                  HubertModel,
                  HubertPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_hubert import (
                  TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFHubertForCTC,
                  TFHubertModel,
                  TFHubertPreTrainedModel,
              )


else:
       import sys

       a	: List[Any]      =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


