




'''simple docstring'''





import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile

import cva
import numpy as np
import requests
import wget
from filelock import FileLock
from PIL import Image
from tqdm.auto import tqdm
from yaml import Loader, dump, load


try:
  import torch

  a		:	List[str]       =      True
except ImportError:
  a		:	str       =      False


try:
  from torch.hub import _get_torch_home

  a		:	Optional[int]       =      _get_torch_home()
except ImportError:
  a		:	List[str]       =      os.path.expanduser(
      os.getenv('''TORCH_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''torch'''))
  )

a		:	int       =      os.path.join(torch_cache_home, '''transformers''')

a		:	int       =      '''https://cdn.huggingface.co'''
a		:	Optional[Any]       =      '''https://s3.amazonaws.com/models.huggingface.co/bert'''
a		:	Union[str, Any]       =      '''/'''.join(str(Path(__file__).resolve()).split('''/''')[:-1])
a		:	Union[str, Any]       =      os.path.join(PATH, '''config.yaml''')
a		:	Optional[Any]       =      os.path.join(PATH, '''attributes.txt''')
a		:	Union[str, Any]       =      os.path.join(PATH, '''objects.txt''')
a		:	int       =      os.getenv('''PYTORCH_PRETRAINED_BERT_CACHE''', default_cache_path)
a		:	str       =      os.getenv('''PYTORCH_TRANSFORMERS_CACHE''', PYTORCH_PRETRAINED_BERT_CACHE)
a		:	List[Any]       =      os.getenv('''TRANSFORMERS_CACHE''', PYTORCH_TRANSFORMERS_CACHE)
a		:	Tuple       =      '''pytorch_model.bin'''
a		:	List[str]       =      '''config.yaml'''





def UpperCAmelCase    (       UpperCAmelCase_=OBJECTS   ,	UpperCAmelCase_=ATTRIBUTES						):
    _a				:							str							       =      []
    with open(UpperCAmelCase_						) as f:
        for object in f.readlines():
            vg_classes.append(object.split(""","""						)[0].lower().strip()						)

    _a				:							Tuple							       =      []
    with open(UpperCAmelCase_						) as f:
        for object in f.readlines():
            vg_attrs.append(object.split(""","""						)[0].lower().strip()						)
    return vg_classes, vg_attrs





def UpperCAmelCase    (       UpperCAmelCase_						):
    _a				:							Tuple							       =      OrderedDict()
    with open(UpperCAmelCase_   ,	"""rb"""						) as f:
        _a				:							Union[str, Any]							       =      pkl.load(UpperCAmelCase_						)["""model"""]
    for k in copy.deepcopy(list(ckp.keys()						)						):
        _a				:							Tuple							       =      ckp.pop(UpperCAmelCase_						)
        if isinstance(UpperCAmelCase_   ,	np.ndarray						):
            _a				:							Tuple							       =      torch.tensor(UpperCAmelCase_						)
        else:
            assert isinstance(UpperCAmelCase_   ,	torch.tensor						), type(UpperCAmelCase_						)
        _a				:							List[Any]							       =      v
    return r




class      UpperCamelCase       :
  a     :Optional[Any]          =					{}



  def __init__(			self		,     __lowerCAmelCase		,     __lowerCAmelCase = "root"		,     __lowerCAmelCase=0 ):







      '''simple docstring'''


      _a				:							Tuple							       =      name
      _a				:							Tuple							       =      level
      _a				:							Dict							       =      {}
      for k, v in dictionary.items():
          if v is None:
              raise ValueError()
          _a				:							Dict							       =      copy.deepcopy(__lowerCAmelCase )
          _a				:							Optional[int]							       =      copy.deepcopy(__lowerCAmelCase )
          if isinstance(__lowerCAmelCase		,     __lowerCAmelCase ):
              _a				:							Optional[Any]							       =      Config(__lowerCAmelCase		,     name=__lowerCAmelCase		,     level=level + 1 )
          _a				:							int							       =      v
          setattr(self		,     __lowerCAmelCase		,     __lowerCAmelCase )

      _a				:							Any							       =      d



  def __repr__(			self ):







      '''simple docstring'''


      return str(list((self._pointer.keys()) ) )



  def __setattr__(			self		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							str							       =      val
      _a				:							List[Any]							       =      val
      _a				:							int							       =      key.split(""".""" )
      _a				:							Optional[Any]							       =      len(__lowerCAmelCase ) - 1
      _a				:							int							       =      self._pointer
      if len(__lowerCAmelCase ) > 1:
          for i, l in enumerate(__lowerCAmelCase ):
              if hasattr(self		,     __lowerCAmelCase ) and isinstance(getattr(self		,     __lowerCAmelCase )		,     __lowerCAmelCase ):
                  setattr(getattr(self		,     __lowerCAmelCase )		,     """.""".join(levels[i:] )		,     __lowerCAmelCase )
              if l == last_level:
                  _a				:							str							       =      val
              else:
                  _a				:							List[str]							       =      pointer[l]



  def     a_							(			self ):







      '''simple docstring'''


      return self._pointer



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      with open(f'''{file_name}'''		,     """w""" ) as stream:
          dump(__lowerCAmelCase		,     __lowerCAmelCase )



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      with open(f'''{file_name}'''		,     """w""" ) as stream:
          json.dump(__lowerCAmelCase		,     __lowerCAmelCase )



  @staticmethod
  def     a_							(			__lowerCAmelCase ):







      '''simple docstring'''


      with open(__lowerCAmelCase ) as stream:
          _a				:							Optional[Any]							       =      load(__lowerCAmelCase		,     Loader=__lowerCAmelCase )
      return data



  def __str__(			self ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      """    """
      if self._name != "root":
          _a				:							str							       =      f'''{t * (self._level-1)}{self._name}:\n'''
      else:
          _a				:							Optional[int]							       =      """"""
      _a				:							Tuple							       =      self._level
      for i, (k, v) in enumerate(self._pointer.items() ):
          if isinstance(__lowerCAmelCase		,     __lowerCAmelCase ):
              r += f'''{t * (self._level)}{v}\n'''
              self._level += 1
          else:
              r += f'''{t * (self._level)}{k}: {v} ({type(__lowerCAmelCase ).__name__})\n'''
          _a				:							Union[str, Any]							       =      level
      return r[:-1]



  @classmethod
  def     a_							(			cls		,     __lowerCAmelCase		,     **__lowerCAmelCase ):







      '''simple docstring'''


      _a       , _a				:							str							       =      cls.get_config_dict(__lowerCAmelCase		,     **__lowerCAmelCase )
      return cls(__lowerCAmelCase )



  @classmethod
  def     a_							(			cls		,     __lowerCAmelCase		,     **__lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Optional[int]							       =      kwargs.pop("""cache_dir"""		,     __lowerCAmelCase )
      _a				:							List[Any]							       =      kwargs.pop("""force_download"""		,     __lowerCAmelCase )
      _a				:							int							       =      kwargs.pop("""resume_download"""		,     __lowerCAmelCase )
      _a				:							Optional[int]							       =      kwargs.pop("""proxies"""		,     __lowerCAmelCase )
      _a				:							List[Any]							       =      kwargs.pop("""local_files_only"""		,     __lowerCAmelCase )

      if os.path.isdir(__lowerCAmelCase ):
          _a				:							str							       =      os.path.join(__lowerCAmelCase		,     __lowerCAmelCase )
      elif os.path.isfile(__lowerCAmelCase ) or is_remote_url(__lowerCAmelCase ):
          _a				:							Union[str, Any]							       =      pretrained_model_name_or_path
      else:
          _a				:							List[Any]							       =      hf_bucket_url(__lowerCAmelCase		,     filename=__lowerCAmelCase		,     use_cdn=__lowerCAmelCase )

      try:
          # Load from URL or cache if already cached
          _a				:							str							       =      cached_path(
              __lowerCAmelCase		,     cache_dir=__lowerCAmelCase		,     force_download=__lowerCAmelCase		,     proxies=__lowerCAmelCase		,     resume_download=__lowerCAmelCase		,     local_files_only=__lowerCAmelCase		,     )
          # Load config dict
          if resolved_config_file is None:
              raise EnvironmentError

          _a				:							Union[str, Any]							       =      Config.load_yaml(__lowerCAmelCase )

      except EnvironmentError:
          _a				:							Union[str, Any]							       =      """Can't load config for"""
          raise EnvironmentError(__lowerCAmelCase )

      if resolved_config_file == config_file:
          print("""loading configuration file from path""" )
      else:
          print("""loading configuration file cache""" )

      return Config.load_yaml(__lowerCAmelCase ), kwargs






def UpperCAmelCase    (       UpperCAmelCase_						):
    _a				:							Optional[int]							       =      torch.load("""dump.pt"""   ,	map_location=in_tensor.device						)
    _a				:							Dict							       =      in_tensor.numpy()
    _a				:							List[Any]							       =      out_tensor.numpy()[0]
    print(na.shape   ,	na[0, 0, :5]						)
    print(na.shape   ,	na[0, 0, :5]						)
    assert np.allclose(UpperCAmelCase_   ,	UpperCAmelCase_   ,	rtol=0.01   ,	atol=0.1						), (
        F'''{sum([1 for x in np.isclose(UpperCAmelCase_   ,	UpperCAmelCase_   ,	rtol=0.01   ,	atol=0.1						).flatten() if x is False]						)/len(na.flatten()						)*100:.4f} %'''
        " element-wise mismatch"
    )
    raise Exception("""tensors are all good"""						)

    # Hugging face functions below





def UpperCAmelCase    (       UpperCAmelCase_						):
    _a				:							int							       =      urlparse(UpperCAmelCase_						)
    return parsed.scheme in ("http", "https")





def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_   ,	UpperCAmelCase_=True						):
    _a				:							List[str]							       =      CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
    _a				:							Union[str, Any]							       =      """/""" not in model_id
    if legacy_format:
        return F'''{endpoint}/{model_id}-{filename}'''
    else:
        return F'''{endpoint}/{model_id}/{filename}'''





def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_   ,	UpperCAmelCase_=None   ,	UpperCAmelCase_=0   ,	UpperCAmelCase_=None   ,	):
    _a				:							Any							       =      """python/{}""".format(sys.version.split()[0]						)
    if _torch_available:
        ua += "; torch/{}".format(torch.__version__						)
    if isinstance(UpperCAmelCase_   ,	UpperCAmelCase_						):
        ua += "; " + "; ".join("""{}/{}""".format(UpperCAmelCase_   ,	UpperCAmelCase_						) for k, v in user_agent.items()						)
    elif isinstance(UpperCAmelCase_   ,	UpperCAmelCase_						):
        ua += "; " + user_agent
    _a				:							Optional[int]							       =      {"""user-agent""": ua}
    if resume_size > 0:
        _a				:							Optional[Any]							       =      """bytes=%d-""" % (resume_size,)
    _a				:							List[Any]							       =      requests.get(UpperCAmelCase_   ,	stream=UpperCAmelCase_   ,	proxies=UpperCAmelCase_   ,	headers=UpperCAmelCase_						)
    if response.status_code == 416:  # Range not satisfiable
        return
    _a				:							List[str]							       =      response.headers.get("""Content-Length"""						)
    _a				:							Any							       =      resume_size + int(UpperCAmelCase_						) if content_length is not None else None
    _a				:							Dict							       =      tqdm(
        unit="""B"""   ,	unit_scale=UpperCAmelCase_   ,	total=UpperCAmelCase_   ,	initial=UpperCAmelCase_   ,	desc="""Downloading"""   ,	)
    for chunk in response.iter_content(chunk_size=1_024						):
        if chunk:  # filter out keep-alive new chunks
            progress.update(len(UpperCAmelCase_						)						)
            temp_file.write(UpperCAmelCase_						)
    progress.close()





def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_=None   ,	UpperCAmelCase_=False   ,	UpperCAmelCase_=None   ,	UpperCAmelCase_=10   ,	UpperCAmelCase_=False   ,	UpperCAmelCase_=None   ,	UpperCAmelCase_=False   ,	):
    if cache_dir is None:
        _a				:							int							       =      TRANSFORMERS_CACHE
    if isinstance(UpperCAmelCase_   ,	UpperCAmelCase_						):
        _a				:							List[Any]							       =      str(UpperCAmelCase_						)

    os.makedirs(UpperCAmelCase_   ,	exist_ok=UpperCAmelCase_						)

    _a				:							int							       =      None
    if not local_files_only:
        try:
            _a				:							Optional[int]							       =      requests.head(UpperCAmelCase_   ,	allow_redirects=UpperCAmelCase_   ,	proxies=UpperCAmelCase_   ,	timeout=UpperCAmelCase_						)
            if response.status_code == 200:
                _a				:							List[Any]							       =      response.headers.get("""ETag"""						)
        except (EnvironmentError, requests.exceptions.Timeout):
            # etag is already None
            pass

    _a				:							Optional[int]							       =      url_to_filename(UpperCAmelCase_   ,	UpperCAmelCase_						)

    # get cache path to put the file
    _a				:							Dict							       =      os.path.join(UpperCAmelCase_   ,	UpperCAmelCase_						)

    # etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
    # try to get the last downloaded one
    if etag is None:
        if os.path.exists(UpperCAmelCase_						):
            return cache_path
        else:
            _a				:							List[Any]							       =      [
                file
                for file in fnmatch.filter(os.listdir(UpperCAmelCase_						)   ,	filename + """.*"""						)
                if not file.endswith(""".json"""						) and not file.endswith(""".lock"""						)
            ]
            if len(UpperCAmelCase_						) > 0:
                return os.path.join(UpperCAmelCase_   ,	matching_files[-1]						)
            else:
                # If files cannot be found and local_files_only=True,
                # the models might've been found if local_files_only=False
                # Notify the user about that
                if local_files_only:
                    raise ValueError(
                        """Cannot find the requested files in the cached path and outgoing traffic has been"""
                        """ disabled. To enable model look-ups and downloads online, set 'local_files_only'"""
                        """ to False."""						)
                return None

    # From now on, etag is not None.
    if os.path.exists(UpperCAmelCase_						) and not force_download:
        return cache_path

    # Prevent parallel downloads of the same file with a lock.
    _a				:							Union[str, Any]							       =      cache_path + """.lock"""
    with FileLock(UpperCAmelCase_						):
        # If the download just completed while the lock was activated.
        if os.path.exists(UpperCAmelCase_						) and not force_download:
            # Even if returning early like here, the lock will be released.
            return cache_path

        if resume_download:
            _a				:							List[str]							       =      cache_path + """.incomplete"""

            @contextmanager
            def _resumable_file_manager():
                with open(UpperCAmelCase_   ,	"""a+b"""						) as f:
                    yield f

            _a				:							Tuple							       =      _resumable_file_manager
            if os.path.exists(UpperCAmelCase_						):
                _a				:							Dict							       =      os.stat(UpperCAmelCase_						).st_size
            else:
                _a				:							List[Any]							       =      0
        else:
            _a				:							Optional[Any]							       =      partial(tempfile.NamedTemporaryFile   ,	dir=UpperCAmelCase_   ,	delete=UpperCAmelCase_						)
            _a				:							str							       =      0

        # Download to temporary file, then copy to cache dir once finished.
        # Otherwise you get corrupt cache entries if the download gets interrupted.
        with temp_file_manager() as temp_file:
            print(
                """%s not found in cache or force_download set to True, downloading to %s"""   ,	UpperCAmelCase_   ,	temp_file.name   ,	)

            http_get(
                UpperCAmelCase_   ,	UpperCAmelCase_   ,	proxies=UpperCAmelCase_   ,	resume_size=UpperCAmelCase_   ,	user_agent=UpperCAmelCase_   ,	)

        os.replace(temp_file.name   ,	UpperCAmelCase_						)

        _a				:							Tuple							       =      {"""url""": url, """etag""": etag}
        _a				:							Tuple							       =      cache_path + """.json"""
        with open(UpperCAmelCase_   ,	"""w"""						) as meta_file:
            json.dump(UpperCAmelCase_   ,	UpperCAmelCase_						)

    return cache_path





def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_=None						):
    _a				:							Dict							       =      url.encode("""utf-8"""						)
    _a				:							List[str]							       =      shaaaa(UpperCAmelCase_						)
    _a				:							Optional[int]							       =      url_hash.hexdigest()

    if etag:
        _a				:							List[str]							       =      etag.encode("""utf-8"""						)
        _a				:							Tuple							       =      shaaaa(UpperCAmelCase_						)
        filename += "." + etag_hash.hexdigest()

    if url.endswith(""".h5"""						):
        filename += ".h5"

    return filename





def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_=None   ,	UpperCAmelCase_=False   ,	UpperCAmelCase_=None   ,	UpperCAmelCase_=False   ,	UpperCAmelCase_=None   ,	UpperCAmelCase_=False   ,	UpperCAmelCase_=False   ,	UpperCAmelCase_=False   ,	):
    if cache_dir is None:
        _a				:							Union[str, Any]							       =      TRANSFORMERS_CACHE
    if isinstance(UpperCAmelCase_   ,	UpperCAmelCase_						):
        _a				:							Any							       =      str(UpperCAmelCase_						)
    if isinstance(UpperCAmelCase_   ,	UpperCAmelCase_						):
        _a				:							int							       =      str(UpperCAmelCase_						)

    if is_remote_url(UpperCAmelCase_						):
        # URL, so get it from the cache (downloading if necessary)
        _a				:							str							       =      get_from_cache(
            UpperCAmelCase_   ,	cache_dir=UpperCAmelCase_   ,	force_download=UpperCAmelCase_   ,	proxies=UpperCAmelCase_   ,	resume_download=UpperCAmelCase_   ,	user_agent=UpperCAmelCase_   ,	local_files_only=UpperCAmelCase_   ,	)
    elif os.path.exists(UpperCAmelCase_						):
        # File, and it exists.
        _a				:							Any							       =      url_or_filename
    elif urlparse(UpperCAmelCase_						).scheme == "":
        # File, but it doesn't exist.
        raise EnvironmentError("""file {} not found""".format(UpperCAmelCase_						)						)
    else:
        # Something unknown
        raise ValueError("""unable to parse {} as a URL or as a local path""".format(UpperCAmelCase_						)						)

    if extract_compressed_file:
        if not is_zipfile(UpperCAmelCase_						) and not tarfile.is_tarfile(UpperCAmelCase_						):
            return output_path

        # Path where we extract compressed archives
        # We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
        _a       , _a				:							Union[str, Any]							       =      os.path.split(UpperCAmelCase_						)
        _a				:							str							       =      output_file.replace("""."""   ,	"""-"""						) + """-extracted"""
        _a				:							Optional[Any]							       =      os.path.join(UpperCAmelCase_   ,	UpperCAmelCase_						)

        if os.path.isdir(UpperCAmelCase_						) and os.listdir(UpperCAmelCase_						) and not force_extract:
            return output_path_extracted

        # Prevent parallel extractions
        _a				:							Optional[int]							       =      output_path + """.lock"""
        with FileLock(UpperCAmelCase_						):
            shutil.rmtree(UpperCAmelCase_   ,	ignore_errors=UpperCAmelCase_						)
            os.makedirs(UpperCAmelCase_						)
            if is_zipfile(UpperCAmelCase_						):
                with ZipFile(UpperCAmelCase_   ,	"""r"""						) as zip_file:
                    zip_file.extractall(UpperCAmelCase_						)
                    zip_file.close()
            elif tarfile.is_tarfile(UpperCAmelCase_						):
                _a				:							List[Any]							       =      tarfile.open(UpperCAmelCase_						)
                tar_file.extractall(UpperCAmelCase_						)
                tar_file.close()
            else:
                raise EnvironmentError("""Archive format of {} could not be identified""".format(UpperCAmelCase_						)						)

        return output_path_extracted

    return output_path





def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_=","						):
    assert isinstance(UpperCAmelCase_   ,	UpperCAmelCase_						)
    if os.path.isfile(UpperCAmelCase_						):
        with open(UpperCAmelCase_						) as f:
            _a				:							Optional[int]							       =      eval(f.read()						)
    else:
        _a				:							Union[str, Any]							       =      requests.get(UpperCAmelCase_						)
        try:
            _a				:							List[str]							       =      requests.json()
        except Exception:
            _a				:							Optional[Any]							       =      req.content.decode()
            assert data is not None, "could not connect"
            try:
                _a				:							Tuple							       =      eval(UpperCAmelCase_						)
            except Exception:
                _a				:							int							       =      data.split("""\n"""						)
        req.close()
    return data





def UpperCAmelCase    (       UpperCAmelCase_						):
    _a				:							Union[str, Any]							       =      requests.get(UpperCAmelCase_						)
    _a				:							Optional[Any]							       =      np.array(Image.open(BytesIO(response.content						)						)						)
    return img





def UpperCAmelCase    (       UpperCAmelCase_						):
    _a				:							int							       =      url.split("""/"""						)[-1]
    if fn not in os.listdir(os.getcwd()						):
        wget.download(UpperCAmelCase_						)
    with open(UpperCAmelCase_   ,	"""rb"""						) as stream:
        _a				:							Optional[int]							       =      pkl.load(UpperCAmelCase_						)
    _a				:							Tuple							       =      weights.pop("""model"""						)
    _a				:							Optional[int]							       =      {}
    for k, v in model.items():
        _a				:							int							       =      torch.from_numpy(UpperCAmelCase_						)
        if "running_var" in k:
            _a				:							Any							       =      torch.tensor([0]						)
            _a				:							List[str]							       =      k.replace("""running_var"""   ,	"""num_batches_tracked"""						)
            _a				:							Tuple							       =      zero
    return new





def UpperCAmelCase    (       ):
    print(F'''{os.path.abspath(os.path.join(UpperCAmelCase_   ,	os.pardir						)						)}/demo.ipynb'''						)





def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_="RGB"						):
    assert isinstance(UpperCAmelCase_   ,	UpperCAmelCase_						)
    if os.path.isfile(UpperCAmelCase_						):
        _a				:							Dict							       =      cva.imread(UpperCAmelCase_						)
    else:
        _a				:							Optional[Any]							       =      get_image_from_url(UpperCAmelCase_						)
        assert img is not None, F'''could not connect to: {im}'''
    _a				:							List[str]							       =      cva.cvtColor(UpperCAmelCase_   ,	cva.COLOR_BGR2RGB						)
    if input_format == "RGB":
        _a				:							Union[str, Any]							       =      img[:, :, ::-1]
    return img





def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_=1						):
    return (images[i : i + batch] for i in range(0   ,	len(UpperCAmelCase_						)   ,	UpperCAmelCase_						))







'''simple docstring'''


import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer






def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







     a_									=args.serialization_dir
     a_									=args.pruning_method
     a_									=args.threshold

     a_									=torch.load(os.path.join(lowercase__							,						"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(6_0							,						" "							)							,						"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(lowercase__							,						lowercase__							).sum().item()
               elif pruning_method == "sigmoied_threshold":
                    a_									=ThresholdBinarizer.apply(lowercase__							,						lowercase__							,						lowercase__							).sum().item()
               elif pruning_method == "l0":
                    a_		,    a_									=-0.1, 1.1
                    a_									=torch.sigmoid(lowercase__							)
                    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(6_0							,						" "							)							,						str(round(1_0_0 * mask_ones / param.numel()							,						3							)							).ljust(2_0							,						" "							)							,						str(lowercase__							)							)
          else:
               encoder_count += param.numel()
               if "bias" in name or "LayerNorm" in name:
                    remaining_count += param.numel()

     print(""							)
     print("Remaining Weights (global) %: "							,						1_0_0 * remaining_count / encoder_count							)


if __name__ == "__main__":
  lowercase											=      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''',
  )

  lowercase											=      parser.parse_args()

  main(args)


