





import argparse
import os
import shutil

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



def _a				(	lowerCamelCase:   str )	->						str:







      '''simple docstring'''






      __A          =				args.pruning_method
      __A          =				args.threshold

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

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

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

      if target_model_path is None:
            __A          =				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__":
       snake_case__					:							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',
       )

       snake_case__					:							Optional[int]													=     parser.parse_args()

       main(args)












import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
       snake_case__					:							Optional[int]													=     argparse.ArgumentParser(
           description=(
               'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'
               ' Distillation'
           )
       )
       parser.add_argument('--model_type', default='bert', choices=['bert'])
       parser.add_argument('--model_name', default='bert-base-uncased', type=str)
       parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)
       parser.add_argument('--vocab_transform', action='store_true')
       snake_case__					:							Optional[int]													=     parser.parse_args()

       if args.model_type == "bert":
              snake_case__					:							Dict													=     BertForMaskedLM.from_pretrained(args.model_name)
              snake_case__					:							Union[str, Any]													=     'bert'
       else:
              raise ValueError('args.model_type should be "bert".')

       snake_case__					:							Optional[int]													=     model.state_dict()
       snake_case__					:							List[Any]													=     {}

       for w in ["word_embeddings", "position_embeddings"]:
              snake_case__					:							Tuple													=     state_dict[f'{prefix}.embeddings.{w}.weight']
       for w in ["weight", "bias"]:
              snake_case__					:							Optional[Any]													=     state_dict[f'{prefix}.embeddings.LayerNorm.{w}']

       snake_case__					:							int													=     0
       for teacher_idx in [0, 2, 4, 7, 9, 11]:
              for w in ["weight", "bias"]:
                     snake_case__					:							Union[str, Any]													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'
                     ]
                     snake_case__					:							Dict													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'
                     ]
                     snake_case__					:							int													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'
                     ]

                     snake_case__					:							int													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'
                     ]
                     snake_case__					:							Optional[int]													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'
                     ]

                     snake_case__					:							Optional[Any]													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'
                     ]
                     snake_case__					:							List[str]													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'
                     ]
                     snake_case__					:							int													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'
                     ]
              std_idx += 1

       snake_case__					:							Optional[int]													=     state_dict['cls.predictions.decoder.weight']
       snake_case__					:							str													=     state_dict['cls.predictions.bias']
       if args.vocab_transform:
              for w in ["weight", "bias"]:
                     snake_case__					:							int													=     state_dict[f'cls.predictions.transform.dense.{w}']
                     snake_case__					:							Optional[int]													=     state_dict[f'cls.predictions.transform.LayerNorm.{w}']

       print(f'N layers selected for distillation: {std_idx}')
       print(f'Number of params transferred for distillation: {len(compressed_sd.keys())}')

       print(f'Save transferred checkpoint to {args.dump_checkpoint}.')
       torch.save(compressed_sd, args.dump_checkpoint)





