





import argparse
import os
import shutil

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





def 					lowerCAmelCase_			(	__a		)					->		int:







  """simple docstring"""





  lowerCamelCase__:				List[Any]        =args.pruning_method
  lowerCamelCase__:				int        =args.threshold

  lowerCamelCase__:				Any        =args.model_name_or_path.rstrip("/"		)
  lowerCamelCase__:				str        =args.target_model_path

  print(F"""Load fine-pruned model from {model_name_or_path}"""		)
  lowerCamelCase__:				int        =torch.load(os.path.join(__a     ,   "pytorch_model.bin"		)		)
  lowerCamelCase__:				Optional[int]        ={}

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

  if target_model_path is None:
    lowerCamelCase__:				Optional[int]        =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__":
     __A     =    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",
     )

     __A     =    parser.parse_args()

     main(args)








import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment



@require_huggingface_suite
class        _SCREAMING_SNAKE_CASE						(     unittest.TestCase   ):







 '''simple docstring'''

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int])	->Tuple:



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =inspect.getfile(accelerate.test_utils)
   lowerCamelCase__:				str        =os.path.sep.join(
       mod_file.split(os.path.sep)[:-1] + ["scripts", "external_deps", "test_metrics.py"])

   from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

   lowerCamelCase__:				str        =test_metrics

 @require_cpu
 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->Any:



   '''simple docstring'''



   debug_launcher(self.test_metrics.main	,			num_processes=1)

 @require_cpu
 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->int:



   '''simple docstring'''



   debug_launcher(self.test_metrics.main)

 @require_single_gpu
 def 							SCREAMING_SNAKE_CASE_ (self    :       Any)	->int:



   '''simple docstring'''



   self.test_metrics.main()







 @require_multi_gpu
 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->Dict:



   '''simple docstring'''



   print(F"""Found {torch.cuda.device_count()} devices.""")
   lowerCamelCase__:				Optional[Any]        =["torchrun", F"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
   with patch_environment(omp_num_threads=1):
     execute_subprocess_async(UpperCAmelCase_	,			env=os.environ.copy())

