





import argparse
import os
import shutil

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

def   __snake_case    (       _lowerCAmelCase			:			Union[str, Any]						)			->    Any:
   A_       :					Dict        =      args.pruning_method
   A_       :					Any        =      args.threshold

   A_       :					Dict        =      args.model_name_or_path.rstrip("/"						)
   A_       :					Optional[Any]        =      args.target_model_path

   print(f"Load fine-pruned model from {model_name_or_path}"						)
   A_       :					int        =      torch.load(os.path.join(_lowerCAmelCase					,					"pytorch_model.bin"						)						)
   A_       :					int        =      {}

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

   if target_model_path is None:
      A_       :					Optional[int]        =      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				:   str						       =						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				:   Dict						       =						parser.parse_args()

 main(args)







from timeit import timeit

_lowerCAmelCase				:   Tuple						       =						{
    '''MALAYALAM''': True,
    '''String''': False,
    '''rotor''': True,
    '''level''': True,
    '''A''': True,
    '''BB''': True,
    '''ABC''': False,
    '''amanaplanacanalpanama''': True,  # "a man a plan a canal panama"
}
# Ensure our test data is valid
assert all((key == key[::-1]) is value for key, value in test_data.items())

def   __snake_case    (       _lowerCAmelCase			:			str						)			->    bool:
   A_       :					List[str]        =      0
   A_       :					str        =      len(_lowerCAmelCase						) - 1
   while start_i < end_i:
      if s[start_i] == s[end_i]:
         start_i += 1
         end_i -= 1
      else:
         return False
   return True

def   __snake_case    (       _lowerCAmelCase			:			str						)			->    bool:
   A_       :					int        =      len(_lowerCAmelCase						) // 2
   A_       :					Union[str, Any]        =      len(_lowerCAmelCase						)

   # We need to traverse till half of the length of string
   # as we can get access of the i'th last element from
   # i'th index.
   # eg: [0,1,2,3,4,5] => 4th index can be accessed
   # with the help of 1st index (i==n-i-1)
   # where n is length of string
   return all(s[i] == s[n - i - 1] for i in range(_lowerCAmelCase						)						)

def   __snake_case    (       _lowerCAmelCase			:			str						)			->    bool:
   if len(_lowerCAmelCase						) <= 2:
      return True
   if s[0] == s[len(_lowerCAmelCase						) - 1]:
      return is_palindrome_recursive(s[1:-1]						)
   else:
      return False

def   __snake_case    (       _lowerCAmelCase			:			str						)			->    bool:
   return s == s[::-1]

def   __snake_case    (       _lowerCAmelCase			:			str						)			->    None:
   A_       :					int        =      f"all({name}(key) is value for key, value in test_data.items())"
   A_       :					List[str]        =      f"from __main__ import test_data, {name}"
   A_       :					str        =      500000
   A_       :					List[str]        =      timeit(stmt=_lowerCAmelCase					,					setup=_lowerCAmelCase					,					number=_lowerCAmelCase						)
   print(f"{name:<35} finished {number:,} runs in {result:.5f} seconds"						)


if __name__ == "__main__":
 for key, value in test_data.items():
  assert is_palindrome(key) is is_palindrome_recursive(key)
  assert is_palindrome(key) is is_palindrome_slice(key)
  print(F'''{key:21} {value}''')
 print('''a man a plan a canal panama''')

 # finished 500,000 runs in 0.46793 seconds
 benchmark_function('''is_palindrome_slice''')
 # finished 500,000 runs in 0.85234 seconds
 benchmark_function('''is_palindrome''')
 # finished 500,000 runs in 1.32028 seconds
 benchmark_function('''is_palindrome_recursive''')
 # finished 500,000 runs in 2.08679 seconds
 benchmark_function('''is_palindrome_traversal''')
