





'''simple docstring'''


import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def 							UpperCAmelCase_  (						__lowerCamelCase					:					str ):
   lowercase_			:Optional[int]    			= args.pruning_method
   lowercase_			:Optional[int]    			= args.threshold

   lowercase_			:Optional[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_			:int    			= torch.load(os.path.join(__lowerCamelCase	,"pytorch_model.bin" ) )
   lowercase_			:List[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_			:Optional[int]    			= tensor
         print(F'Copied layer {name}' )
      elif "bias" in name:
         lowercase_			:str    			= tensor
         print(F'Copied layer {name}' )
      else:
         if pruning_method == "magnitude":
            lowercase_			:List[str]    			= MagnitudeBinarizer.apply(inputs=__lowerCamelCase	,threshold=__lowerCamelCase )
            lowercase_			:Any    			= tensor * mask
            print(F'Pruned layer {name}' )
         elif pruning_method == "topK":
            if "mask_scores" in name:
               continue
            lowercase_			:Optional[Any]    			= name[:-6]
            lowercase_			:Any    			= model[F'{prefix_}mask_scores']
            lowercase_			:Union[str, Any]    			= TopKBinarizer.apply(__lowerCamelCase	,__lowerCamelCase )
            lowercase_			:List[str]    			= tensor * mask
            print(F'Pruned layer {name}' )
         elif pruning_method == "sigmoied_threshold":
            if "mask_scores" in name:
               continue
            lowercase_			:Optional[Any]    			= name[:-6]
            lowercase_			:Optional[Any]    			= model[F'{prefix_}mask_scores']
            lowercase_			:Dict    			= ThresholdBinarizer.apply(__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase )
            lowercase_			:Union[str, Any]    			= tensor * mask
            print(F'Pruned layer {name}' )
         elif pruning_method == "l0":
            if "mask_scores" in name:
               continue
            lowercase_			:str    			= name[:-6]
            lowercase_			:Dict    			= model[F'{prefix_}mask_scores']
            lowercase_			,							lowercase_			:Optional[Any]    			= -0.1, 1.1
            lowercase_			:Dict    			= torch.sigmoid(__lowerCamelCase )
            lowercase_			:List[str]    			= s * (r - l) + l
            lowercase_			:Any    			= s_bar.clamp(min=0.0	,max=1.0 )
            lowercase_			:str    			= tensor * mask
            print(F'Pruned layer {name}' )
         else:
            raise ValueError("Unknown pruning method" )

   if target_model_path is None:
      lowercase_			:List[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__":
 lowerCAmelCase					:     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''',
 )

 lowerCAmelCase					:     int												=parser.parse_args()

 main(args)









'''simple docstring'''


import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

lowerCAmelCase					:     Optional[int]												=logging.getLogger()
def 							UpperCAmelCase_  (						):
   lowercase_			:Union[str, Any]    			= argparse.ArgumentParser()
   parser.add_argument("-f" )
   lowercase_			:Any    			= parser.parse_args()
   return args.f


class       a_   (				_lowerCAmelCase							):





     def 							lowercase__      ( self		:					List[Any]						):





        """simple docstring"""




        lowercase_			:List[Any]    			= logging.StreamHandler(sys.stdout						)
        logger.addHandler(lowercase						)





     def 							lowercase__      ( self		:					str	,      lowercase		:					str						):





        """simple docstring"""




        lowercase_			:Dict    			= get_gpu_count()

        if n_gpu > 1:
           pass
           # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
           # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
           # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
           # cmd = [sys.executable] + distributed_args + args
           # execute_subprocess_async(cmd, env=self.get_env())
           # XXX: test the results - need to save them first into .json file
        else:
           args.insert(0	,      "run_glue_deebert.py"						)
           with patch.object(lowercase	,      "argv"	,      lowercase						):
              lowercase_			:int    			= run_glue_deebert.main()
              for value in result.values():
                 self.assertGreaterEqual(lowercase	,      0.6_66						)







     @slow
     @require_torch_non_multi_gpu
     def 							lowercase__      ( self		:					Tuple						):





        """simple docstring"""




        lowercase_			:List[Any]    			= "\n            --model_type roberta\n            --model_name_or_path roberta-base\n            --task_name MRPC\n            --do_train\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --max_seq_length 128\n            --per_gpu_eval_batch_size=1\n            --per_gpu_train_batch_size=8\n            --learning_rate 2e-4\n            --num_train_epochs 3\n            --overwrite_output_dir\n            --seed 42\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --save_steps 0\n            --overwrite_cache\n            --eval_after_first_stage\n            ".split()
        self.run_and_check(lowercase						)

        lowercase_			:List[str]    			= "\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --eval_each_highway\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            ".split()
        self.run_and_check(lowercase						)

        lowercase_			:Any    			= "\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --early_exit_entropy 0.1\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            ".split()
        self.run_and_check(lowercase						)


