



'''simple docstring'''


import argparse
import os
import shutil

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







def 				__lowerCamelCase				(   A__    )       ->	Optional[int]:






   """simple docstring"""
   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(A__    ,						'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=A__    ,						threshold=A__    )
            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(A__    ,						A__    )
            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(A__    ,						A__    ,						A__    )
            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(A__    )
            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(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__":
   _lowerCamelCase	:       Union[str, Any]      	=	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	:       List[Any]      	=	parser.parse_args()

   main(args)






'''simple docstring'''


def 				__lowerCamelCase				(   A__ = 50    )       ->	int:






   """simple docstring"""
   UpperCamelCase					=  [1] * (length + 1)

   for row_length in range(3    ,						length + 1    ):
      for block_length in range(3    ,						row_length + 1    ):
         for block_start in range(row_length - block_length    ):
            ways_number[row_length] += ways_number[
                row_length - block_start - block_length - 1
            ]

         ways_number[row_length] += 1

   return ways_number[length]


if __name__ == "__main__":
   print(f'''{solution() = }''')

