


import argparse
import os
import shutil

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

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optional[Any]    ):
     __UpperCamelCase   							=args.pruning_method
     __UpperCamelCase   							=args.threshold

     __UpperCamelCase   							=args.model_name_or_path.rstrip('/'    )
     __UpperCamelCase   							=args.target_model_path

     print(F'Load fine-pruned model from {model_name_or_path}'    )
     __UpperCamelCase   							=torch.load(os.path.join(SCREAMING_SNAKE_CASE__      ,					'pytorch_model.bin'    )    )
     __UpperCamelCase   							={}

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

     if target_model_path is None:
          __UpperCamelCase   							=os.path.join(
              os.path.dirname(SCREAMING_SNAKE_CASE__    )      ,					F'bertarized_{os.path.basename(SCREAMING_SNAKE_CASE__    )}'    )

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

     torch.save(SCREAMING_SNAKE_CASE__      ,					os.path.join(SCREAMING_SNAKE_CASE__      ,					'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
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler






@dataclass
class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		int              =  0
    UpperCAmelCase__						:		bool              =  False
    UpperCAmelCase__						:		float              =  3.0






class    UpperCAmelCase__	( unittest.TestCase		):



    """simple docstring"""






    def _a (      self   )  ->	Any:
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         self.assertDictEqual(MockClass().to_kwargs() ,       {}   )
         self.assertDictEqual(MockClass(a=2   ).to_kwargs() ,       {'a': 2}   )
         self.assertDictEqual(MockClass(a=2 ,       b=A_   ).to_kwargs() ,       {'a': 2, 'b': True}   )
         self.assertDictEqual(MockClass(a=2 ,       c=2.25   ).to_kwargs() ,       {'a': 2, 'c': 2.25}   )






    @require_cuda
    def _a (      self   )  ->	Union[str, Any]:
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         __UpperCamelCase   							=GradScalerKwargs(init_scale=1024 ,       growth_factor=2   )
         AcceleratorState._reset_state()
         __UpperCamelCase   							=Accelerator(mixed_precision='fp16' ,       kwargs_handlers=[scaler_handler]   )
         print(accelerator.use_fpaa   )
         __UpperCamelCase   							=accelerator.scaler

         # Check the kwargs have been applied
         self.assertEqual(scaler._init_scale ,       1024.0   )
         self.assertEqual(scaler._growth_factor ,       2.0   )

         # Check the other values are at the default
         self.assertEqual(scaler._backoff_factor ,       0.5   )
         self.assertEqual(scaler._growth_interval ,       2000   )
         self.assertEqual(scaler._enabled ,       A_   )






    @require_multi_gpu
    def _a (      self   )  ->	Optional[int]:
         __UpperCamelCase   							=['torchrun', f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__   )]
         execute_subprocess_async(A_ ,       env=os.environ.copy()   )


if __name__ == "__main__":
   _A			=						DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
   _A			=						Accelerator(kwargs_handlers=[ddp_scaler])
   _A			=						torch.nn.Linear(100, 200)
   _A			=						accelerator.prepare(model)

   # Check the values changed in kwargs
   _A			=						''
   _A			=						model.bucket_bytes_cap // (1024 * 1024)
   if observed_bucket_cap_map != 15:
      error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
   if model.find_unused_parameters is not True:
      error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

   # Check the values of the defaults
   if model.dim != 0:
      error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
   if model.broadcast_buffers is not True:
      error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
   if model.gradient_as_bucket_view is not False:
      error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

   # Raise error at the end to make sure we don't stop at the first failure.
   if len(error_msg) > 0:
      raise ValueError(error_msg)





