





import argparse
import os
import shutil

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


def     lowerCamelCase_	(      lowerCAmelCase:	Any				)->		Tuple:
  _snake_case   :				str														=	args.pruning_method
  _snake_case   :				Any														=	args.threshold

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

  print(F"""Load fine-pruned model from {model_name_or_path}"""				)
  _snake_case   :				Dict														=	torch.load(os.path.join(lowerCAmelCase    ,							'pytorch_model.bin'				)				)
  _snake_case   :				Union[str, Any]														=	{}

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

  if target_model_path is None:
    _snake_case   :				Optional[Any]														=	os.path.join(
        os.path.dirname(lowerCAmelCase				)    ,							F"""bertarized_{os.path.basename(lowerCAmelCase				)}"""				)

  if not os.path.isdir(lowerCAmelCase				):
    shutil.copytree(lowerCAmelCase    ,							lowerCAmelCase				)
    print(F"""\nCreated folder {target_model_path}"""				)

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


if __name__ == "__main__":
      lowerCAmelCase_						   =    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""",
      )

      lowerCAmelCase_						   =    parser.parse_args()

      main(args)











from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters


def     lowerCamelCase_	(      )->		Any:
  _snake_case   :				List[str]														=	{
      'repo_name': ['test_repo1', 'test_repo2', 'test_repo3'],
      'path': ['test_1.py', 'test_2.py', 'unit_test.py'],
      'content': ['a ' * 20, 'a ' * 30, 'b ' * 7],
  }
  _snake_case   :				Optional[Any]														=	Dataset.from_dict(lowerCAmelCase				)
  return dataset







class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''







  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    _snake_case   :				Union[str, Any]														=	get_dataset()
    _snake_case   :				Tuple														=	make_duplicate_clusters(UpperCamelCase      ,				0.85							)
    self.assertEqual(len(duplicate_clusters[0]							)      ,				2							)







  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    _snake_case   :				List[str]														=	get_dataset()
    _snake_case							,					_snake_case   :				str														=	deduplicate_dataset(UpperCamelCase							)
    self.assertEqual(len(UpperCamelCase							)      ,				2							)
    print(UpperCamelCase							)
    self.assertEqual(duplicate_clusters[0][0]['copies']      ,				2							)
    self.assertEqual(duplicate_clusters[0][0]['is_extreme']      ,				UpperCamelCase							)




