


'''simple docstring'''




from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter


def    UpperCAmelCase_  (       A		,	A		,	A = 1 / sqrt(2      )      ):
   '''simple docstring'''





   _a					:  List[Any]						=  tau * frequency / samplerate
   _a					:  Tuple						=  sin(A      )
   _a					:  List[Any]						=  cos(A      )
   _a					:  Union[str, Any]						=  _sin / (2 * q_factor)

   _a					:  Dict						=  (1 - _cos) / 2
   _a					:  Any						=  1 - _cos

   _a					:  Any						=  1 + alpha
   _a					:  int						=  -2 * _cos
   _a					:  str						=  1 - alpha

   _a					:  Optional[Any]						=  IIRFilter(2      )
   filt.set_coefficients([aa, aa, aa]		,	[ba, ba, ba]      )
   return filt


def    UpperCAmelCase_  (       A		,	A		,	A = 1 / sqrt(2      )      ):
   '''simple docstring'''





   _a					:  int						=  tau * frequency / samplerate
   _a					:  int						=  sin(A      )
   _a					:  Union[str, Any]						=  cos(A      )
   _a					:  int						=  _sin / (2 * q_factor)

   _a					:  Dict						=  (1 + _cos) / 2
   _a					:  int						=  -1 - _cos

   _a					:  Optional[int]						=  1 + alpha
   _a					:  str						=  -2 * _cos
   _a					:  Dict						=  1 - alpha

   _a					:  List[str]						=  IIRFilter(2      )
   filt.set_coefficients([aa, aa, aa]		,	[ba, ba, ba]      )
   return filt


def    UpperCAmelCase_  (       A		,	A		,	A = 1 / sqrt(2      )      ):
   '''simple docstring'''





   _a					:  str						=  tau * frequency / samplerate
   _a					:  Dict						=  sin(A      )
   _a					:  int						=  cos(A      )
   _a					:  Dict						=  _sin / (2 * q_factor)

   _a					:  List[Any]						=  _sin / 2
   _a					:  List[str]						=  0
   _a					:  Dict						=  -ba

   _a					:  List[Any]						=  1 + alpha
   _a					:  Union[str, Any]						=  -2 * _cos
   _a					:  List[Any]						=  1 - alpha

   _a					:  int						=  IIRFilter(2      )
   filt.set_coefficients([aa, aa, aa]		,	[ba, ba, ba]      )
   return filt


def    UpperCAmelCase_  (       A		,	A		,	A = 1 / sqrt(2      )      ):
   '''simple docstring'''





   _a					:  Optional[Any]						=  tau * frequency / samplerate
   _a					:  Tuple						=  sin(A      )
   _a					:  Tuple						=  cos(A      )
   _a					:  Dict						=  _sin / (2 * q_factor)

   _a					:  List[Any]						=  1 - alpha
   _a					:  int						=  -2 * _cos
   _a					:  List[Any]						=  1 + alpha

   _a					:  str						=  IIRFilter(2      )
   filt.set_coefficients([ba, ba, ba]		,	[ba, ba, ba]      )
   return filt


def    UpperCAmelCase_  (       A		,	A		,	A		,	A = 1 / sqrt(2      )		,	):
   '''simple docstring'''





   _a					:  Union[str, Any]						=  tau * frequency / samplerate
   _a					:  str						=  sin(A      )
   _a					:  str						=  cos(A      )
   _a					:  List[Any]						=  _sin / (2 * q_factor)
   _a					:  Optional[Any]						=  1_0 ** (gain_db / 4_0)

   _a					:  Dict						=  1 + alpha * big_a
   _a					:  str						=  -2 * _cos
   _a					:  Tuple						=  1 - alpha * big_a
   _a					:  Tuple						=  1 + alpha / big_a
   _a					:  str						=  -2 * _cos
   _a					:  Union[str, Any]						=  1 - alpha / big_a

   _a					:  Optional[int]						=  IIRFilter(2      )
   filt.set_coefficients([aa, aa, aa]		,	[ba, ba, ba]      )
   return filt


def    UpperCAmelCase_  (       A		,	A		,	A		,	A = 1 / sqrt(2      )		,	):
   '''simple docstring'''





   _a					:  Optional[int]						=  tau * frequency / samplerate
   _a					:  List[str]						=  sin(A      )
   _a					:  Tuple						=  cos(A      )
   _a					:  Union[str, Any]						=  _sin / (2 * q_factor)
   _a					:  str						=  1_0 ** (gain_db / 4_0)
   _a					:  Optional[Any]						=  (big_a + 1) - (big_a - 1) * _cos
   _a					:  List[str]						=  (big_a + 1) + (big_a - 1) * _cos
   _a					:  List[Any]						=  (big_a - 1) - (big_a + 1) * _cos
   _a					:  Dict						=  (big_a - 1) + (big_a + 1) * _cos
   _a					:  Tuple						=  2 * sqrt(A      ) * alpha

   _a					:  Any						=  big_a * (pmc + aaa)
   _a					:  Optional[int]						=  2 * big_a * mpc
   _a					:  Dict						=  big_a * (pmc - aaa)
   _a					:  List[str]						=  ppmc + aaa
   _a					:  int						=  -2 * pmpc
   _a					:  Tuple						=  ppmc - aaa

   _a					:  Tuple						=  IIRFilter(2      )
   filt.set_coefficients([aa, aa, aa]		,	[ba, ba, ba]      )
   return filt







def    UpperCAmelCase_  (       A		,	A		,	A		,	A = 1 / sqrt(2      )		,	):
   '''simple docstring'''





   _a					:  Dict						=  tau * frequency / samplerate
   _a					:  Tuple						=  sin(A      )
   _a					:  Any						=  cos(A      )
   _a					:  int						=  _sin / (2 * q_factor)
   _a					:  str						=  1_0 ** (gain_db / 4_0)
   _a					:  List[Any]						=  (big_a + 1) - (big_a - 1) * _cos
   _a					:  Union[str, Any]						=  (big_a + 1) + (big_a - 1) * _cos
   _a					:  List[Any]						=  (big_a - 1) - (big_a + 1) * _cos
   _a					:  List[str]						=  (big_a - 1) + (big_a + 1) * _cos
   _a					:  Union[str, Any]						=  2 * sqrt(A      ) * alpha

   _a					:  Optional[Any]						=  big_a * (ppmc + aaa)
   _a					:  List[str]						=  -2 * big_a * pmpc
   _a					:  Any						=  big_a * (ppmc - aaa)
   _a					:  List[Any]						=  pmc + aaa
   _a					:  Tuple						=  2 * mpc
   _a					:  List[Any]						=  pmc - aaa

   _a					:  str						=  IIRFilter(2      )
   filt.set_coefficients([aa, aa, aa]		,	[ba, ba, ba]      )
   return filt






'''simple docstring'''




import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer


def    UpperCAmelCase_  (       A      ):
   '''simple docstring'''





   _a					:  Dict						=  args.pruning_method
   _a					:  Optional[Any]						=  args.threshold

   _a					:  Union[str, Any]						=  args.model_name_or_path.rstrip('/'      )
   _a					:  Dict						=  args.target_model_path

   print(f'''Load fine-pruned model from {model_name_or_path}'''      )
   _a					:  List[str]						=  torch.load(os.path.join(A		,	'pytorch_model.bin'      )      )
   _a					:  str						=  {}

   for name, tensor in model.items():
      if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
         _a					:  List[Any]						=  tensor
         print(f'''Copied layer {name}'''      )
      elif "classifier" in name or "qa_output" in name:
         _a					:  List[str]						=  tensor
         print(f'''Copied layer {name}'''      )
      elif "bias" in name:
         _a					:  Optional[Any]						=  tensor
         print(f'''Copied layer {name}'''      )
      else:
         if pruning_method == "magnitude":
            _a					:  Tuple						=  MagnitudeBinarizer.apply(inputs=A		,	threshold=A      )
            _a					:  Union[str, Any]						=  tensor * mask
            print(f'''Pruned layer {name}'''      )
         elif pruning_method == "topK":
            if "mask_scores" in name:
               continue
            _a					:  str						=  name[:-6]
            _a					:  int						=  model[f'''{prefix_}mask_scores''']
            _a					:  List[str]						=  TopKBinarizer.apply(A		,	A      )
            _a					:  Tuple						=  tensor * mask
            print(f'''Pruned layer {name}'''      )
         elif pruning_method == "sigmoied_threshold":
            if "mask_scores" in name:
               continue
            _a					:  str						=  name[:-6]
            _a					:  List[str]						=  model[f'''{prefix_}mask_scores''']
            _a					:  Tuple						=  ThresholdBinarizer.apply(A		,	A		,	A      )
            _a					:  Union[str, Any]						=  tensor * mask
            print(f'''Pruned layer {name}'''      )
         elif pruning_method == "l0":
            if "mask_scores" in name:
               continue
            _a					:  Optional[int]						=  name[:-6]
            _a					:  Any						=  model[f'''{prefix_}mask_scores''']
            _a		,						_a					:  Optional[int]						=  -0.1, 1.1
            _a					:  List[str]						=  torch.sigmoid(A      )
            _a					:  Dict						=  s * (r - l) + l
            _a					:  int						=  s_bar.clamp(min=0.0		,	max=1.0      )
            _a					:  List[str]						=  tensor * mask
            print(f'''Pruned layer {name}'''      )
         else:
            raise ValueError('Unknown pruning method'      )

   if target_model_path is None:
      _a					:  Tuple						=  os.path.join(
          os.path.dirname(A      )		,	f'''bertarized_{os.path.basename(A      )}'''      )

   if not os.path.isdir(A      ):
      shutil.copytree(A		,	A      )
      print(f'''\nCreated folder {target_model_path}'''      )

   torch.save(A		,	os.path.join(A		,	'pytorch_model.bin'      )      )
   print('\nPruned model saved! See you later!'      )


if __name__ == "__main__":
 UpperCAmelCase_      :		str          =		argparse.ArgumentParser()

 parser.add_argument(
     "--pruning_method",
     choices=["l0", "magnitude", "topK", "sigmoied_threshold"],
     type=str,
     required=True,
     help=(
         "Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,"
         " sigmoied_threshold = Soft movement pruning)"
     ),
 )
 parser.add_argument(
     "--threshold",
     type=float,
     required=False,
     help=(
         "For `magnitude` and `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(
     "--model_name_or_path",
     type=str,
     required=True,
     help="Folder containing the model that was previously fine-pruned",
 )
 parser.add_argument(
     "--target_model_path",
     default=None,
     type=str,
     required=False,
     help="Folder containing the model that was previously fine-pruned",
 )

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

 main(args)


