import argparse
import os
import shutil

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





def  _lowerCamelCase					(   a_				: str):
   lowerCamelCase						:Optional[Any]						  =     args.pruning_method
   lowerCamelCase						:List[Any]						  =     args.threshold

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

   print(F"Load fine-pruned model from {model_name_or_path}")
   lowerCamelCase						:Union[str, Any]						  =     torch.load(os.path.join(a_						,			'''pytorch_model.bin'''))
   lowerCamelCase						:List[str]						  =     {}

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

   if target_model_path is None:
      lowerCamelCase						:Tuple						  =     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__           =							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 json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE      ,   unittest.TestCase				):
							_UpperCAmelCase						    =     DebertaTokenizer
							_UpperCAmelCase						    =     True
							_UpperCAmelCase						    =     DebertaTokenizerFast







							def 				snake_case  (				self     :    List[str]		):
										super().setUp()

										# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
										lowerCamelCase						:Dict						  =     [
										    '''l''',
										    '''o''',
										    '''w''',
										    '''e''',
										    '''r''',
										    '''s''',
										    '''t''',
										    '''i''',
										    '''d''',
										    '''n''',
										    '''\u0120''',
										    '''\u0120l''',
										    '''\u0120n''',
										    '''\u0120lo''',
										    '''\u0120low''',
										    '''er''',
										    '''\u0120lowest''',
										    '''\u0120newer''',
										    '''\u0120wider''',
										    '''[UNK]''',
										]
										lowerCamelCase						:List[str]						  =     dict(zip(__snake_case	,					range(len(__snake_case		)		)		)		)
										lowerCamelCase						:Any						  =     ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
										lowerCamelCase						:Dict						  =     {'''unk_token''': '''[UNK]'''}

										lowerCamelCase						:Any						  =     os.path.join(self.tmpdirname	,					VOCAB_FILES_NAMES['''vocab_file''']		)
										lowerCamelCase						:List[str]						  =     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(__snake_case		) + '''\n'''		)
										with open(self.merges_file	,					'''w'''	,					encoding='''utf-8'''		) as fp:
													fp.write('''\n'''.join(__snake_case		)		)







							def 				snake_case  (				self     :    str	,					**__snake_case     :    Dict		):
										kwargs.update(self.special_tokens_map		)
										return self.tokenizer_class.from_pretrained(self.tmpdirname	,					**__snake_case		)







							def 				snake_case  (				self     :    Optional[Any]	,					__snake_case     :    int		):
										lowerCamelCase						:List[Any]						  =     '''lower newer'''
										lowerCamelCase						:List[str]						  =     '''lower newer'''
										return input_text, output_text







							def 				snake_case  (				self     :    str		):
										lowerCamelCase						:Optional[int]						  =     self.get_tokenizer()
										lowerCamelCase						:Union[str, Any]						  =     '''lower newer'''
										lowerCamelCase						:str						  =     ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
										lowerCamelCase						:Optional[int]						  =     tokenizer.tokenize(__snake_case		)
										self.assertListEqual(__snake_case	,					__snake_case		)

										lowerCamelCase						:List[str]						  =     tokens + [tokenizer.unk_token]
										lowerCamelCase						:Optional[int]						  =     [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case		)	,					__snake_case		)







							def 				snake_case  (				self     :    Optional[int]		):
										lowerCamelCase						:List[str]						  =     self.get_tokenizer()
										lowerCamelCase						:Optional[int]						  =     tokenizer('''Hello'''	,					'''World'''		)
										lowerCamelCase						:List[str]						  =     [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
										self.assertListEqual(tokd['''token_type_ids''']	,					__snake_case		)







							@slow
							def 				snake_case  (				self     :    str		):
										lowerCamelCase						:Union[str, Any]						  =     self.tokenizer_class.from_pretrained('''microsoft/deberta-base'''		)

										lowerCamelCase						:Optional[Any]						  =     tokenizer.encode('''sequence builders'''	,					add_special_tokens=__snake_case		)
										lowerCamelCase						:Tuple						  =     tokenizer.encode('''multi-sequence build'''	,					add_special_tokens=__snake_case		)

										lowerCamelCase						:Union[str, Any]						  =     tokenizer.encode(
										    '''sequence builders'''	,					add_special_tokens=__snake_case	,					add_prefix_space=__snake_case		)
										lowerCamelCase						:str						  =     tokenizer.encode(
										    '''sequence builders'''	,					'''multi-sequence build'''	,					add_special_tokens=__snake_case	,					add_prefix_space=__snake_case		)

										lowerCamelCase						:Any						  =     tokenizer.build_inputs_with_special_tokens(__snake_case		)
										lowerCamelCase						:Dict						  =     tokenizer.build_inputs_with_special_tokens(__snake_case	,					__snake_case		)

										assert encoded_sentence == encoded_text_from_decode
										assert encoded_pair == encoded_pair_from_decode







							@slow
							def 				snake_case  (				self     :    str		):
										lowerCamelCase						:List[str]						  =     [self.tokenizer_class]
										if self.test_rust_tokenizer:
													tokenizer_classes.append(self.rust_tokenizer_class		)

										for tokenizer_class in tokenizer_classes:
													lowerCamelCase						:int						  =     tokenizer_class.from_pretrained('''microsoft/deberta-base'''		)

													lowerCamelCase						:Tuple						  =     [
													    '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
													    '''ALBERT incorporates two parameter reduction techniques''',
													    '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
													    ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
													    ''' vocabulary embedding.''',
													]

													lowerCamelCase						:List[Any]						  =     tokenizer(__snake_case	,					padding=__snake_case		)
													lowerCamelCase						:Union[str, Any]						  =     [tokenizer.decode(__snake_case	,					skip_special_tokens=__snake_case		) for seq in encoding['''input_ids''']]

													# fmt: off
													lowerCamelCase						:Any						  =     {
													    '''input_ids''': [
													        [1, 2118, 11126, 565, 35, 83, 25191, 163, 18854, 13, 12156, 12, 16101, 25376, 13807, 9, 22205, 27893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
													        [1, 2118, 11126, 565, 24536, 80, 43797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
													        [1, 133, 78, 65, 16, 10, 3724, 1538, 33183, 11303, 43797, 1938, 4, 870, 24165, 29105, 5, 739, 32644, 33183, 11303, 36173, 88, 80, 650, 7821, 45940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 13171, 31, 5, 1836, 9, 32644, 33183, 11303, 4, 2]
													    ],
													    '''token_type_ids''': [
													        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
													        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
													        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
													    ],
													    '''attention_mask''': [
													        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
													        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
													        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
													    ]
													}
													# fmt: on

													lowerCamelCase						:Optional[int]						  =     [
													    '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
													    '''ALBERT incorporates two parameter reduction techniques''',
													    '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
													    ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
													    ''' vocabulary embedding.''',
													]

													self.assertDictEqual(encoding.data	,					__snake_case		)

													for expected, decoded in zip(__snake_case	,					__snake_case		):
																self.assertEqual(__snake_case	,					__snake_case		)



