
"""simple docstring"""




import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer

def a__   (	A_ ):






  '''simple docstring'''





  __magic_name__    					=	args.serialization_dir
  __magic_name__    					=	args.pruning_method
  __magic_name__    					=	args.threshold

  __magic_name__    					=	torch.load(os.path.join(_UpperCAmelCase,					"""pytorch_model.bin""" ),					map_location="""cpu""" )

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

    main(args)



import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger

def a__   (	A_,					A_ ):






  '''simple docstring'''





  __magic_name__    					=	copy.deepcopy(A_ )
  __magic_name__    					=	sorted(A_ )
  __magic_name__    					=	[bisect.bisect_right(A_,					A_ ) for y in x]
  return quantized

def a__   (	A_,					A_=0 ):






  '''simple docstring'''





  __magic_name__    					=	np.arange(start=3,					stop=A_,					step=4 ).tolist() if k > 0 else [10]
  __magic_name__    					=	_quantize(A_,					A_ )
  # count number of elements per group
  __magic_name__    					=	np.unique(A_,					return_counts=A_ )[1]
  __magic_name__    					=	[0] + bins + [np.inf]
  logger.info("""Using {} as bins for aspect lengths quantization""".format(A_ ) )
  logger.info("""Count of instances per bin: {}""".format(A_ ) )
  return groups





class 	UpperCAmelCase_      (			_A					):


  '''simple docstring'''







  def __init__(			self      :			Dict      ,							UpperCamelCase__      :			Dict      ,							UpperCamelCase__      :			Optional[Any]      ,							UpperCamelCase__      :			Tuple   )      ->     Union[str, Any]:







    """simple docstring"""






    if not isinstance(UpperCamelCase__      ,							UpperCamelCase__   ):
      raise ValueError(
          """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(UpperCamelCase__   )   )
    __magic_name__    					=	sampler
    __magic_name__    					=	group_ids
    __magic_name__    					=	batch_size
  def __iter__(			self      :			Tuple   )      ->     Union[str, Any]:







    """simple docstring"""






    __magic_name__    					=	defaultdict(UpperCamelCase__   )
    __magic_name__    					=	defaultdict(UpperCamelCase__   )

    __magic_name__    					=	0
    for idx in self.sampler:
      __magic_name__    					=	self.group_ids[idx]
      buffer_per_group[group_id].append(UpperCamelCase__   )
      samples_per_group[group_id].append(UpperCamelCase__   )
      if len(buffer_per_group[group_id]   ) == self.batch_size:
        yield buffer_per_group[group_id]  # TODO
        num_batches += 1
        del buffer_per_group[group_id]
      assert len(buffer_per_group[group_id]   ) < self.batch_size

    # now we have run out of elements that satisfy
    # the group criteria, let's return the remaining
    # elements so that the size of the sampler is
    # deterministic
    __magic_name__    					=	len(self   )
    __magic_name__    					=	expected_num_batches - num_batches
    if num_remaining > 0:
      # for the remaining batches, group the batches by similar lengths
      __magic_name__    					=	[]
      for group_id, idxs in sorted(buffer_per_group.items()      ,							key=lambda UpperCamelCase__   : x[0]   ):
        batch_idx.extend(UpperCamelCase__   )
        if len(UpperCamelCase__   ) >= self.batch_size:
          yield batch_idx[: self.batch_size]
          __magic_name__    					=	batch_idx[self.batch_size :]
          num_remaining -= 1
      if len(UpperCamelCase__   ) > 0:
        yield batch_idx
        num_remaining -= 1
    assert num_remaining == 0





  def __len__(			self      :			List[Any]   )      ->     Any:







    """simple docstring"""






    return (len(self.sampler   ) + self.batch_size - 1) // self.batch_size

