


import argparse
import os
import shutil

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




def     UpperCamelCase    (      _a		)					->							Tuple:
     '''simple docstring'''





     lowercase_       :int     =       args.pruning_method
     lowercase_       :List[str]     =       args.threshold

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

     print(f"Load fine-pruned model from {model_name_or_path}"		)
     lowercase_       :Optional[int]     =       torch.load(os.path.join(_lowerCamelCase       ,		'''pytorch_model.bin'''		)		)
     lowercase_       :str     =       {}

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

     if target_model_path is None:
          lowercase_       :Union[str, 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__":
   SCREAMING_SNAKE_CASE       :		int         =  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",
   )

   SCREAMING_SNAKE_CASE       :		str         =  parser.parse_args()

   main(args)





def     UpperCamelCase    (      _a       ,		_a		)					->							int:
     '''simple docstring'''





     while a != 0:
          lowercase_    ,      lowercase_       :Union[str, Any]     =       b % a, a
     return b




def     UpperCamelCase    (      _a       ,		_a		)					->							int:
     '''simple docstring'''





     if gcd(_a       ,		_a		) != 1:
          lowercase_       :Union[str, Any]     =       f"mod inverse of {a!r} and {m!r} does not exist"
          raise ValueError(_a		)
     lowercase_    ,      lowercase_    ,      lowercase_       :Any     =       1, 0, a
     lowercase_    ,      lowercase_    ,      lowercase_       :List[str]     =       0, 1, m
     while va != 0:
          lowercase_       :Tuple     =       ua // va
          lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_       :Optional[int]     =       (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
     return ua % m

