



import argparse
import os
import shutil

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

def 	_lowerCAmelCase (    A__						):
      lowercase__  =	args.pruning_method
      lowercase__  =	args.threshold

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

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

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

      if target_model_path is None:
            lowercase__  =	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__   :					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",
       )

       a__   :					Any 						=      parser.parse_args()

       main(args)





import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class    UpperCAmelCase__(				lowerCamelCase  ,				unittest.TestCase						):

   '''simple docstring'''




   A     :	int												=     CTRLTokenizer
   A     :	Tuple												=     False
   A     :	Any												=     False


   def       UpperCAmelCase		(    self	:      int)						->					Any:




         """simple docstring"""

         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         lowercase__  =	['adapt', 're@@', 'a@@', 'apt', 'c@@', 't', '<unk>']
         lowercase__  =	dict(zip(lowerCAmelCase      ,					range(len(lowerCAmelCase))))
         lowercase__  =	['#version: 0.2', 'a p', 'ap t</w>', 'r e', 'a d', 'ad apt</w>', '']
         lowercase__  =	{'unk_token': '<unk>'}

         lowercase__  =	os.path.join(self.tmpdirname      ,					VOCAB_FILES_NAMES['vocab_file'])
         lowercase__  =	os.path.join(self.tmpdirname      ,					VOCAB_FILES_NAMES['merges_file'])
         with open(self.vocab_file      ,					'w'      ,					encoding='utf-8') as fp:
               fp.write(json.dumps(lowerCAmelCase) + '\n')
         with open(self.merges_file      ,					'w'      ,					encoding='utf-8') as fp:
               fp.write('\n'.join(lowerCAmelCase))


   def       UpperCAmelCase		(    self	:      int      ,					**lowerCAmelCase	:      str)						->					Dict:




         """simple docstring"""

         kwargs.update(self.special_tokens_map)
         return CTRLTokenizer.from_pretrained(self.tmpdirname      ,					**lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      List[str]      ,					lowerCAmelCase	:      Optional[int])						->					Optional[Any]:




         """simple docstring"""

         lowercase__  =	'adapt react readapt apt'
         lowercase__  =	'adapt react readapt apt'
         return input_text, output_text



   def       UpperCAmelCase		(    self	:      Optional[int])						->					str:




         """simple docstring"""

         lowercase__  =	CTRLTokenizer(self.vocab_file      ,					self.merges_file      ,					**self.special_tokens_map)
         lowercase__  =	'adapt react readapt apt'
         lowercase__  =	'adapt re@@ a@@ c@@ t re@@ adapt apt'.split()
         lowercase__  =	tokenizer.tokenize(lowerCAmelCase)
         self.assertListEqual(lowerCAmelCase      ,					lowerCAmelCase)

         lowercase__  =	tokens + [tokenizer.unk_token]

         lowercase__  =	[0, 1, 2, 4, 5, 1, 0, 3, 6]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase)      ,					lowerCAmelCase)
